Intelligent Search Modification Guidance

ABSTRACT

A low-latency database analysis system outputs visualization data for presenting a visualization representing results data responsive to the request for data, and, in response to outputting the visualization data, generates a diverse plurality of candidate modifications for the request for data, outputs candidate modification data for presenting the diverse plurality of candidate modifications in association with the presentation of the visualization representing the results data, in response to outputting the candidate modification data, obtains user input data identifying a candidate modification from the diverse plurality of candidate modifications as a selected modification, in response to the user input data, executes a data-query expressing the request for data modified by the selected modification to obtain second results data responsive to the request for data modified by the selected modification, and in response to the second results data, outputs visualization data for presenting a visualization representing the second results data.

CROSS-REFERENCE TO RELATED APPLICATION(S)

This application is a continuation of U.S. application patent Ser. No.16/528,266, filed Jul. 31, 2019, the entire disclosure of which ishereby incorporated by reference.

BACKGROUND

Advances in computer storage and database technology have led toexponential growth of the amount of data being created. Businesses areoverwhelmed by the volume of the data stored in their computer systems.Existing database analytic tools are inefficient, costly to utilize,and/or require substantial configuration and training.

SUMMARY

Disclosed herein are implementations of methods and systems for use in alow-latency database analysis system.

An aspect of the disclosure is a method for use in a low-latencydatabase analysis system, including obtaining, by a low-latency databaseanalysis system, data expressing a usage intent with respect to thelow-latency database analysis system, in response to obtaining the dataexpressing the usage intent, obtaining an analytical-object representinga request for data indicated in the data expressing the usage intent, inresponse to obtaining the analytical-object, obtaining first resultsdata responsive to the request for data, in response to obtaining thefirst results data, outputting visualization data for presenting avisualization representing the first results data, and, in response tooutputting the visualization data, generating a diverse plurality ofcandidate modifications for the request for data, outputting candidatemodification data for presenting the diverse plurality of candidatemodifications in association with presenting the visualizationrepresenting the results data, in response to outputting the candidatemodification data, obtaining user input data identifying a candidatemodification from the diverse plurality of candidate modifications as aselected modification, in response to obtaining the user input data,executing a data-query expressing the request for data modified by theselected modification to obtain second results data responsive to therequest for data modified by the selected modification, and in responseto obtaining the second results data, outputting visualization data forpresenting a visualization representing the second results data.Generating the diverse plurality of candidate modifications for therequest for data includes generating a first candidate modification formodifying the request for data in accordance with a first modificationtype, and a second candidate modification for modifying the request fordata in accordance with a second modification type that differs from thefirst modification type.

Another aspect of the disclosure is a low-latency database analysissystem, comprising a memory that stores executable instructions, and aprocessor that executes the instructions to obtain data expressing ausage intent with respect to the low-latency database analysis system,in response to the data expressing the usage intent, obtain ananalytical-object representing a request for data indicated in the dataexpressing the usage intent, in response to the analytical-object,obtain first results data responsive to the request for data, inresponse to the first results data, output visualization data forpresenting a visualization representing the first results data, and, inresponse to outputting the visualization data, generate a diverseplurality of candidate modifications for the request for data, outputcandidate modification data for presenting the diverse plurality ofcandidate modifications in association with the presentation of thevisualization representing the results data, in response to outputtingthe candidate modification data, obtain user input data identifying acandidate modification from the diverse plurality of candidatemodifications as a selected modification, in response to the user inputdata, execute a data-query expressing the request for data modified bythe selected modification to obtain second results data responsive tothe request for data modified by the selected modification, and inresponse to the second results data, output visualization data forpresenting a visualization representing the second results data. Togenerate the diverse plurality of candidate modifications the processorexecutes the instructions to generate a first candidate modification formodifying the request for data in accordance with a first modificationtype, and a second candidate modification for modifying the request fordata in accordance with a second modification type that differs from thefirst modification type.

Another aspect of the disclosure is a non-transitory computer-readablestorage medium, comprising executable instructions such that executionof the instructions by a processor includes obtaining, by a low-latencydatabase analysis system, data expressing a usage intent with respect tothe low-latency database analysis system, in response to obtaining thedata expressing the usage intent, obtaining an analytical-objectrepresenting a request for data indicated in the data expressing theusage intent, in response to obtaining the analytical-object, obtainingfirst results data responsive to the request for data, in response toobtaining the first results data, outputting visualization data forpresenting a visualization representing the first results data, and, inresponse to outputting the visualization data, generating a diverseplurality of candidate modifications for the request for data,outputting candidate modification data for presenting the diverseplurality of candidate modifications in association with presenting thevisualization representing the results data, in response to outputtingthe candidate modification data, obtaining user input data identifying acandidate modification from the diverse plurality of candidatemodifications as a selected modification, in response to obtaining theuser input data, executing a data-query expressing the request for datamodified by the selected modification to obtain second results dataresponsive to the request for data modified by the selectedmodification, and in response to obtaining the second results data,outputting visualization data for presenting a visualizationrepresenting the second results data. Generating the diverse pluralityof candidate modifications for the request for data includes generatinga first candidate modification for modifying the request for data inaccordance with a first modification type, and a second candidatemodification for modifying the request for data in accordance with asecond modification type that differs from the first modification type.

BRIEF DESCRIPTION OF THE DRAWINGS

The disclosure is best understood from the following detaileddescription when read in conjunction with the accompanying drawings. Itis emphasized that, according to common practice, the various featuresof the drawings are not to-scale. On the contrary, the dimensions of thevarious features may be arbitrarily expanded or reduced for clarity.

FIG. 1 is a block diagram of an example of a computing device.

FIG. 2 is a block diagram of an example of a computing system.

FIG. 3 is a block diagram of an example of a low-latency databaseanalysis system.

FIG. 4 is diagram of an example of a display region generated forpresenting a user interface to facilitate use of intelligent searchguidance.

FIG. 5 is a block diagram of an example of a modification determinationunit in a low-latency database analysis system.

FIG. 6 is a flowchart of an example of intelligent search guidance for alow-latency database analysis system.

DETAILED DESCRIPTION

Businesses and other organizations store large amounts of data, such asbusiness records, transaction records, and the like, in data storagesystems, such as relational database systems that store data as records,or rows, having values, or fields, corresponding to respective columnsin tables that can be interrelated using key values. Databasesstructures are often normalized or otherwise organized to maximize datadensity and to maximize transactional data operations at the expense ofincreased complexity and reduced accessibility for analysis. Individualrecords and tables may have little or no utility without substantialcorrelation, interpretation, and analysis. The complexity of these datastructures and the large volumes of data that can be stored thereinlimit the accessibility of the data and require substantial skilledhuman resources to code procedures and tools that allow business usersto access useful data. The tools that are available for accessing thesesystems are limited to outputting data expressly requested by the usersand lack the capability to identify and prioritize data other than thedata expressly requested. Useful data, such as data aggregations,patterns, and statistical anomalies that would not be available insmaller data sets (e.g., 10,000 rows of data), and may not be apparentto human users, may be derivable using the large volume of data (e.g.,millions or billions of rows) stored in complex data storage systems,such as relational database systems, but may be inaccessible due to thecomplexity and limitations of the data storage systems.

A low-latency database analysis system may use multiple search grammarsfor processing input data, such as input data representing user input.Methods for educating users on how to use the search grammars in anefficient and useful manner may be needed. Help pages and customersupport provide some level of training but the learning curve may be abit steep. Moreover, as the system evolves and new terminology is added,it would be easier to expose such new aspects in the established contextof an existing search result or visualization. Consequently, users maynot be leveraging the system to its capabilities.

The systems and methods for intelligent search modification guidancedisclosed herein enable viewing and simple execution of modificationswhich may obtain search results that may be extensions of or related toa visualization or search result currently being viewed. For example,the modifications may be search constructs which may provide additionalor useful extensions of displayed search results. The modifications mayhave probabilistic high utility. In an implementation, the modificationsmay include, but is not limited to, additive modifications, subtractivemodifications, comparative modifications, filter modifications, and thelike. The low-latency database analysis system may determine whichmodifications to surface based on the search results, a context of thesearch results, an attribute of the search results, a feature of thesearch results, usage, utility, or the like, and combinations thereof.In an implementation, the low-latency database analysis system may use aprobabilistic combination of the search results, usage, request for datalogs, user profile, underlying data distribution, domain knowledge,context, device type, session, social signals, previous modifications,user feedback, insights, utility data, usage data, feedback, and thelike.

The modifications surfaced by the low-latency database analysis systemmay guide or educate users on search terminology and search constructionwith respect to current and future requests for data and on what type ofmodifications may have a high probability of utility in obtainingrelevant search results. For example, these modifications may provideguidance to users on what the best modification may be to get toactionable insights more efficiently and effectively. For example, thelow-latency database analysis system may surface modifications which mayshowcase interesting search concepts in context of the currentvisualization or request for data that is powering the displayed searchresults. If a visualization currently shows a bar chart for a requestfor data “revenue by color”, then a surfaced modification may be“popular color choices” based on usage or other factors in the system.In another example, the low-latency database analysis system may surfacea search construct and action which may signal an outlier or an anomalyin a currently viewed search result.

Intelligent search modification guidance provides better perspectives onhow to use the low-latency database analysis system. For example,previously surfaced modifications may be leveraged to formulate andconstruct new requests for data with an appreciation for what the searchterminology provides or enables. A real-time tutorial may be provided bythe low-latency database analysis system via intelligent searchmodification guidance. For example, a constrained search field may beprovided to enter newly learned modifications to filter or operate onthe currently viewed visualization or search results. That is, thefilter search experience may be influenced by the context of thecurrently viewed visualization or search results.

Intelligent search modification guidance exposes the users to new searchterminology in an established context of an existing search result thata user understands. For example, the low-latency database analysissystem may surface modifications which introduce new search terminologyhaving new relational search capabilities. Consequently, users maybecome more effective and efficient in inputting requests for data andobtaining search results.

FIG. 1 is a block diagram of an example of a computing device 1000. Oneor more aspects of this disclosure may be implemented using thecomputing device 1000. The computing device 1000 includes a processor1100, static memory 1200, low-latency memory 1300, an electroniccommunication unit 1400, a user interface 1500, a bus 1600, and a powersource 1700. Although shown as a single unit, any one or more element ofthe computing device 1000 may be integrated into any number of separatephysical units. For example, the low-latency memory 1300 and theprocessor 1100 may be integrated in a first physical unit and the userinterface 1500 may be integrated in a second physical unit. Although notshown in FIG. 1, the computing device 1000 may include other aspects,such as an enclosure or one or more sensors.

The computing device 1000 may be a stationary computing device, such asa personal computer (PC), a server, a workstation, a minicomputer, or amainframe computer; or a mobile computing device, such as a mobiletelephone, a personal digital assistant (PDA), a laptop, or a tablet PC.

The processor 1100 may include any device or combination of devicescapable of manipulating or processing a signal or other information,including optical processors, quantum processors, molecular processors,or a combination thereof. The processor 1100 may be a central processingunit (CPU), such as a microprocessor, and may include one or moreprocessing units, which may respectively include one or more processingcores. The processor 1100 may include multiple interconnectedprocessors. For example, the multiple processors may be hardwired ornetworked, including wirelessly networked. In some implementations, theoperations of the processor 1100 may be distributed across multiplephysical devices or units that may be coupled directly or across anetwork. In some implementations, the processor 1100 may include acache, or cache memory, for internal storage of operating data orinstructions. The processor 1100 may include one or more special purposeprocessors, one or more digital signal processor (DSP), one or moremicroprocessors, one or more controllers, one or more microcontrollers,one or more integrated circuits, one or more an Application SpecificIntegrated Circuits, one or more Field Programmable Gate Array, one ormore programmable logic arrays, one or more programmable logiccontrollers, firmware, one or more state machines, or any combinationthereof.

The processor 1100 may be operatively coupled with the static memory1200, the low-latency memory 1300, the electronic communication unit1400, the user interface 1500, the bus 1600, the power source 1700, orany combination thereof. The processor may execute, which may includecontrolling, such as by sending electronic signals to, receivingelectronic signals from, or both, the static memory 1200, thelow-latency memory 1300, the electronic communication unit 1400, theuser interface 1500, the bus 1600, the power source 1700, or anycombination thereof to execute, instructions, programs, code,applications, or the like, which may include executing one or moreaspects of an operating system, and which may include executing one ormore instructions to perform one or more aspects described herein, aloneor in combination with one or more other processors.

The static memory 1200 is coupled to the processor 1100 via the bus 1600and may include non-volatile memory, such as a disk drive, or any formof non-volatile memory capable of persistent electronic informationstorage, such as in the absence of an active power supply. Althoughshown as a single block in FIG. 1, the static memory 1200 may beimplemented as multiple logical or physical units.

The static memory 1200 may store executable instructions or data, suchas application data, an operating system, or a combination thereof, foraccess by the processor 1100. The executable instructions may beorganized into programmable modules or algorithms, functional programs,codes, code segments, or combinations thereof to perform one or moreaspects, features, or elements described herein. The application datamay include, for example, user files, database catalogs, configurationinformation, or a combination thereof. The operating system may be, forexample, a desktop or laptop operating system; an operating system for amobile device, such as a smartphone or tablet device; or an operatingsystem for a large device, such as a mainframe computer.

The low-latency memory 1300 is coupled to the processor 1100 via the bus1600 and may include any storage medium with low-latency data accessincluding, for example, DRAM modules such as DDR SDRAM, Phase-ChangeMemory (PCM), flash memory, or a solid-state drive. Although shown as asingle block in FIG. 1, the low-latency memory 1300 may be implementedas multiple logical or physical units. Other configurations may be used.For example, low-latency memory 1300, or a portion thereof, andprocessor 1100 may be combined, such as by using a system on a chipdesign.

The low-latency memory 1300 may store executable instructions or data,such as application data for low-latency access by the processor 1100.The executable instructions may include, for example, one or moreapplication programs, that may be executed by the processor 1100. Theexecutable instructions may be organized into programmable modules oralgorithms, functional programs, codes, code segments, and/orcombinations thereof to perform various functions described herein.

The low-latency memory 1300 may be used to store data that is analyzedor processed using the systems or methods described herein. For example,storage of some or all data in low-latency memory 1300 instead of staticmemory 1200 may improve the execution speed of the systems and methodsdescribed herein by permitting access to data more quickly by an orderof magnitude or greater (e.g., nanoseconds instead of microseconds).

The electronic communication unit 1400 is coupled to the processor 1100via the bus 1600. The electronic communication unit 1400 may include oneor more transceivers. The electronic communication unit 1400 may, forexample, provide a connection or link to a network via a networkinterface. The network interface may be a wired network interface, suchas Ethernet, or a wireless network interface. For example, the computingdevice 1000 may communicate with other devices via the electroniccommunication unit 1400 and the network interface using one or morenetwork protocols, such as Ethernet, Transmission ControlProtocol/Internet Protocol (TCP/IP), power line communication (PLC),Wi-Fi, infrared, ultra violet (UV), visible light, fiber optic, wireline, general packet radio service (GPRS), Global System for Mobilecommunications (GSM), code-division multiple access (CDMA), Long-TermEvolution (LTE), or other suitable protocols.

The user interface 1500 may include any unit capable of interfacing witha human user, such as a virtual or physical keypad, a touchpad, adisplay, a touch display, a speaker, a microphone, a video camera, asensor, a printer, or any combination thereof. For example, a keypad canconvert physical input of force applied to a key to an electrical signalthat can be interpreted by computing device 1000. In another example, adisplay can convert electrical signals output by computing device 1000to light. The purpose of such devices may be to permit interaction witha human user, for example by accepting input from the human user andproviding output back to the human user. The user interface 1500 mayinclude a display; a positional input device, such as a mouse, touchpad,touchscreen, or the like; a keyboard; or any other human and machineinterface device. The user interface 1500 may be coupled to theprocessor 1100 via the bus 1600. In some implementations, the userinterface 1500 can include a display, which can be a liquid crystaldisplay (LCD), a cathode-ray tube (CRT), a light emitting diode (LED)display, an organic light emitting diode (OLED) display, an activematrix organic light emitting diode (AMOLED), or other suitable display.In some implementations, the user interface 1500, or a portion thereof,may be part of another computing device (not shown). For example, aphysical user interface, or a portion thereof, may be omitted from thecomputing device 1000 and a remote or virtual interface may be used,such as via the electronic communication unit 1400.

The bus 1600 is coupled to the static memory 1200, the low-latencymemory 1300, the electronic communication unit 1400, the user interface1500, and the power source 1700. Although a single bus is shown in FIG.1, the bus 1600 may include multiple buses, which may be connected, suchas via bridges, controllers, or adapters.

The power source 1700 provides energy to operate the computing device1000. The power source 1700 may be a general-purpose alternating-current(AC) electric power supply, or power supply interface, such as aninterface to a household power source. In some implementations, thepower source 1700 may be a single use battery or a rechargeable batteryto allow the computing device 1000 to operate independently of anexternal power distribution system. For example, the power source 1700may include a wired power source; one or more thy cell batteries, suchas nickel-cadmium (NiCad), nickel-zinc (NiZn), nickel metal hydride(NiMH), lithium-ion (Li-ion); solar cells; fuel cells; or any otherdevice capable of powering the computing device 1000.

FIG. 2 is a block diagram of an example of a computing system 2000. Asshown, the computing system 2000 includes an external data sourceportion 2100, an internal database analysis portion 2200, and a systeminterface portion 2300. The computing system 2000 may include otherelements not shown in FIG. 2, such as computer network elements.

The external data source portion 2100 may be associated with, such ascontrolled by, an external person, entity, or organization(second-party). The internal database analysis portion 2200 may beassociated with, such as created by or controlled by, a person, entity,or organization (first-party). The system interface portion 2300 may beassociated with, such as created by or controlled by, the first-partyand may be accessed by the first-party, the second-party, third-parties,or a combination thereof, such as in accordance with access andauthorization permissions and procedures.

The external data source portion 2100 is shown as including externaldatabase servers 2120 and external application servers 2140. Theexternal data source portion 2100 may include other elements not shownin FIG. 2. The external data source portion 2100 may include externalcomputing devices, such as the computing device 1000 shown in FIG. 1,which may be used by or accessible to the external person, entity, ororganization (second-party) associated with the external data sourceportion 2100, including but not limited to external database servers2120 and external application servers 2140. The external computingdevices may include data regarding the operation of the external person,entity, or organization (second-party) associated with the external datasource portion 2100.

The external database servers 2120 may be one or more computing devicesconfigured to store data in a format and schema determined externallyfrom the internal database analysis portion 2200, such as by asecond-party associated with the external data source portion 2100, or athird party. For example, the external database server 2120 may use arelational database and may include a database catalog with a schema. Insome embodiments, the external database server 2120 may include anon-database data storage structure, such as a text-based datastructure, such as a comma separated variable structure or an extensiblemarkup language formatted structure or file. For example, the externaldatabase servers 2120 can include data regarding the production ofmaterials by the external person, entity, or organization (second-party)associated with the external data source portion 2100, communicationsbetween the external person, entity, or organization (second-party)associated with the external data source portion 2100 and third parties,or a combination thereof. Other data may be included. The externaldatabase may be a structured database system, such as a relationaldatabase operating in a relational database management system (RDBMS),which may be an enterprise database. In some embodiments, the externaldatabase may be an unstructured data source. The external data mayinclude data or content, such as sales data, revenue data, profit data,tax data, shipping data, safety data, sports data, health data, weatherdata, or the like, or any other data, or combination of data, that maybe generated by or associated with a user, an organization, or anenterprise and stored in a database system. For simplicity and clarity,data stored in or received from the external data source portion 2100may be referred to herein as enterprise data.

The external application server 2140 may include application software,such as application software used by the external person, entity, ororganization (second-party) associated with the external data sourceportion 2100. The external application server 2140 may include data ormetadata relating to the application software.

The external database servers 2120, the external application servers2140, or both, shown in FIG. 2 may represent logical units or devicesthat may be implemented on one or more physical units or devices, whichmay be controlled or operated by the first party, the second party, or athird party.

The external data source portion 2100, or aspects thereof, such as theexternal database servers 2120, the external application servers 2140,or both, may communicate with the internal database analysis portion2200, or an aspect thereof, such as one or more of the servers 2220,2240, 2260, and 2280, via an electronic communication medium, which maybe a wired or wireless electronic communication medium. For example, theelectronic communication medium may include a local area network (LAN),a wide area network (WAN), a fiber channel network, the Internet, or acombination thereof.

The internal database analysis portion 2200 is shown as includingservers 2220, 2240, 2260, and 2280. The servers 2220, 2240, 2260, and2280 may be computing devices, such as the computing device 1000 shownin FIG. 1. Although four servers 2220, 2240, 2260, and 2280 are shown inFIG. 2, other numbers, or cardinalities, of servers may be used. Forexample, the number of computing devices may be determined based on thecapability of individual computing devices, the amount of data to beprocessed, the complexity of the data to be processed, or a combinationthereof. Other metrics may be used for determining the number ofcomputing devices.

The internal database analysis portion 2200 may store data, processdata, or store and process data. The internal database analysis portion2200 may include a distributed cluster (not expressly shown) which mayinclude two or more of the servers 2220, 2240, 2260, and 2280. Theoperation of distributed cluster, such as the operation of the servers2220, 2240, 2260, and 2280 individually, in combination, or both, may bemanaged by a distributed cluster manager. For example, the server 2220may be the distributed cluster manager. In another example, thedistributed cluster manager may be implemented on another computingdevice (not shown). The data and processing of the distributed clustermay be distributed among the servers 2220, 2240, 2260, and 2280, such asby the distributed cluster manager.

Enterprise data from the external data source portion 2100, such as fromthe external database server 2120, the external application server 2140,or both may be imported into the internal database analysis portion2200. The external database server 2120, the external application server2140, or both may be one or more computing devices and may communicatewith the internal database analysis portion 2200 via electroniccommunication. The imported data may be distributed among, processed by,stored on, or a combination thereof, one or more of the servers 2220,2240, 2260, and 2280. Importing the enterprise data may includeimporting or accessing the data structures of the enterprise data.Importing the enterprise data may include generating internal data,internal data structures, or both, based on the enterprise data. Theinternal data, internal data structures, or both may accuratelyrepresent and may differ from the enterprise data, the data structuresof the enterprise data, or both. In some implementations, enterprisedata from multiple external data sources may be imported into theinternal database analysis portion 2200. For simplicity and clarity,data stored or used in the internal database analysis portion 2200 maybe referred to herein as internal data. For example, the internal data,or a portion thereof, may represent, and may be distinct from,enterprise data imported into or accessed by the internal databaseanalysis portion 2200.

The system interface portion 2300 may include one or more client devices2320, 2340. The client devices 2320, 2340 may be computing devices, suchas the computing device 1000 shown in FIG. 1. For example, one of theclient devices 2320, 2340 may be a desktop or laptop computer and theother of the client devices 2320, 2340 may be a mobile device,smartphone, or tablet. One or more of the client devices 2320, 2340 mayaccess the internal database analysis portion 2200. For example, theinternal database analysis portion 2200 may provide one or moreservices, application interfaces, or other electronic computercommunication interfaces, such as a web site, and the client devices2320, 2340 may access the interfaces provided by the internal databaseanalysis portion 2200, which may include accessing the internal datastored in the internal database analysis portion 2200.

In an example, one or more of the client devices 2320, 2340 may send amessage or signal indicating a request for data, which may include arequest for data analysis, to the internal database analysis portion2200. The internal database analysis portion 2200 may receive andprocess the request, which may include distributing the processing amongone or more of the servers 2220, 2240, 2260, and 2280, may generate aresponse to the request, which may include generating or modifyinginternal data, internal data structures, or both, and may output theresponse to the client device 2320, 2340 that sent the request.Processing the request may include accessing one or more internal dataindexes, an internal database, or a combination thereof. The clientdevice 2320, 2340 may receive the response, including the response dataor a portion thereof, and may store, output, or both, the response or arepresentation thereof, such as a representation of the response data,or a portion thereof, which may include presenting the representationvia a user interface on a presentation device of the client device 2320,2340, such as to a user of the client device 2320, 2340.

The system interface portion 2300, or aspects thereof, such as one ormore of the client devices 2320, 2340, may communicate with the internaldatabase analysis portion 2200, or an aspect thereof, such as one ormore of the servers 2220, 2240, 2260, and 2280, via an electroniccommunication medium, which may be a wired or wireless electroniccommunication medium. For example, the electronic communication mediummay include a local area network (LAN), a wide area network (WAN), afiber channel network, the Internet, or a combination thereof.

FIG. 3 is a block diagram of an example of a low-latency databaseanalysis system 3000. The low-latency database analysis system 3000, oraspects thereof, may be similar to the internal database analysisportion 2200 shown in FIG. 2, except as described herein or otherwiseclear from context. The low-latency database analysis system 3000, oraspects thereof, may be implemented on one or more computing devices,such as servers 2220, 2240, 2260, and 2280 shown in FIG. 2, which may bein a clustered or distributed computing configuration.

The low-latency database analysis system 3000 may store and maintain theinternal data, or a portion thereof, such as low-latency data, in alow-latency memory device, such as the low-latency memory 1300 shown inFIG. 1, or any other type of data storage medium or combination of datastorage devices with relatively fast (low-latency) data access,organized in a low-latency data structure. In some embodiments, thelow-latency database analysis system 3000 may be implemented as one ormore logical devices in a cloud-based configuration optimized forautomatic database analysis.

As shown, the low-latency database analysis system 3000 includes adistributed cluster manager 3100, a security and governance unit 3200, adistributed in-memory database 3300, an enterprise data interface unit3400, a distributed in-memory ontology unit 3500, a semantic interfaceunit 3600, a relational search unit 3700, a natural language processingunit 3710, a data utility unit 3720, an insight unit 3730, an objectsearch unit 3800, an object utility unit 3810, a system configurationunit 3820, a user customization unit 3830, a system access interfaceunit 3900, a real-time collaboration unit 3910, and a third-partyintegration unit 3920, which may be collectively referred to as thecomponents of the low-latency database analysis system 3000.

Although not expressly shown in FIG. 3, one or more of the components ofthe low-latency database analysis system 3000 may be implemented on oneor more operatively connected physical or logical computing devices,such as in a distributed cluster computing configuration, such as theinternal database analysis portion 2200 shown in FIG. 2. Although shownseparately in FIG. 3, one or more of the components of the low-latencydatabase analysis system 3000, or respective aspects thereof, may becombined or otherwise organized.

The low-latency database analysis system 3000 may include different,fewer, or additional components not shown in FIG. 3. The aspects orcomponents implemented in an instance of the low-latency databaseanalysis system 3000 may be configurable. For example, the insight unit3730 may be omitted or disabled. One or more of the components of thelow-latency database analysis system 3000 may be implemented in a mannersuch that aspects thereof are divided or combined into variousexecutable modules or libraries in a manner which may differ from thatdescribed herein.

The low-latency database analysis system 3000 may implement anapplication programming interface (API), which may monitor, receive, orboth, input signals or messages from external devices and systems,client systems, process received signals or messages, transmitcorresponding signals or messages to one or more of the components ofthe low-latency database analysis system 3000, and output, such astransmit or send, output messages or signals to respective externaldevices or systems. The low-latency database analysis system 3000 may beimplemented in a distributed computing configuration.

The distributed cluster manager 3100 manages the operative configurationof the low-latency database analysis system 3000. Managing the operativeconfiguration of the low-latency database analysis system 3000 mayinclude controlling the implementation of and distribution of processingand storage across one or more logical devices operating on one or morephysical devices, such as the servers 2220, 2240, 2260, and 2280 shownin FIG. 2. The distributed cluster manager 3100 may generate andmaintain configuration data for the low-latency database analysis system3000, such as in one or more tables, identifying the operativeconfiguration of the low-latency database analysis system 3000. Forexample, the distributed cluster manager 3100 may automatically updatethe low-latency database analysis system configuration data in responseto an operative configuration event, such as a change in availability orperformance for a physical or logical unit of the low-latency databaseanalysis system 3000. One or more of the component units of low-latencydatabase analysis system 3000 may access the database analysis systemconfiguration data, such as to identify intercommunication parameters orpaths.

The security and governance unit 3200 may describe, implement, enforce,or a combination thereof, rules and procedures for controlling access toaspects of the low-latency database analysis system 3000, such as theinternal data of the low-latency database analysis system 3000 and thefeatures and interfaces of the low-latency database analysis system3000. The security and governance unit 3200 may apply security at anontological level to control or limit access to the internal data of thelow-latency database analysis system 3000, such as to columns, tables,rows, or fields, which may include using row level security.

Although shown as a single unit in FRI. 3, the distributed in-memorydatabase 3300 may be implemented in a distributed configuration, such asdistributed among the servers 2220, 2240, 2260, and 2280 shown in FIG.2, which may include multiple in-memory database instances. Eachin-memory database instance may utilize one or more distinct resources,such as processing or low-latency memory resources, that differ from theresources utilized by the other in-memory database instances. In someembodiments, the in-memory database instances may utilize one or moreshared resources, such as resources utilized by two or more in-memorydatabase instances.

The distributed in-memory database 3300 may generate, maintain, or both,a low-latency data structure and data stored or maintained therein(low-latency data). The low-latency data may include principal data,which may represent enterprise data, such as enterprise data importedfrom an external enterprise data source, such as the external datasource portion 2100 shown in FIG. 2, In some implementations, thedistributed in-memory database 3300 may include system internal datarepresenting one or more aspects, features, or configurations of thelow-latency database analysis system 3000. The distributed in-memorydatabase 3300 and the low-latency data stored therein, or a portionthereof, may be accessed using commands, messages, or signals inaccordance with a defined structured query language associated with thedistributed in-memory database 3300.

The low-latency data, or a portion thereof, may be organized as tablesin the distributed in-memory database 3300. A table may be a datastructure to organize or group the data or a portion thereof, such asrelated or similar data. A table may have a defined structure. Forexample, each table may define or describe a respective set of one ormore columns.

A column may define or describe the characteristics of a discrete aspectof the data in the table. For example, the definition or description ofa column may include an identifier, such as a name, for the columnwithin the table, and one or more constraints, such as a data type, forthe data corresponding to the column in the table. The definition ordescription of a column may include other information, such as adescription of the column. The data in a table may be accessible orpartitionable on a per-column basis. The set of tables, including thecolumn definitions therein, and information describing relationshipsbetween elements, such as tables and columns, of the database may bedefined or described by a database schema or design. The cardinality ofcolumns of a table, and the definition and organization of the columns,may be defined by the database schema or design. Adding, deleting, ormodifying a table, a column, the definition thereof, or a relationshipor constraint thereon, may be a modification of the database design,schema, model, or structure.

The low-latency data, or a portion thereof, may be stored in thedatabase as one or more rows or records in respective tables. Eachrecord or row of a table may include a respective field or cellcorresponding to each column of the table. A field may store a discretedata value. The cardinality of rows of a table, and the values storedtherein, may be variable based on the data. Adding, deleting, ormodifying rows, or the data stored therein may omit modification of thedatabase design, schema, or structure. The data stored in respectivecolumns may be identified or defined as a measure data, attribute data,or enterprise ontology data (e.g., metadata).

Measure data, or measure values, may include quantifiable or additivenumeric values, such as integer or floating-point values, which mayinclude numeric values indicating sizes, amounts, degrees, or the like.A column defined as representing measure values may be referred toherein as a measure or fact. A measure may be a property on whichquantitative operations (e.g., sum, count, average, minimum, maximum)may be performed to calculate or determine a result or output.

Attribute data, or attribute values, may include non-quantifiablevalues, such as text or image data, which may indicate names anddescriptions, quantifiable values designated, defined, or identified asattribute data, such as numeric unit identifiers, or a combinationthereof. A column defined as including attribute values may be referredto herein as an attribute or dimension. For example, attributes mayinclude text, identifiers, timestamps, or the like.

Enterprise ontology data may include data that defines or describes oneor more aspects of the database, such as data that describes one or moreaspects of the attributes, measures, rows, columns, tables,relationships, or other aspects of the data or database schema. Forexample, a portion of the database design, model, or schema may berepresented as enterprise ontology data in one or more tables in thedatabase.

Distinctly identifiable data in the low-latency data may be referred toherein as a data portion. For example, the low-latency data stored inthe distributed in-memory database 3300 may be referred to herein as adata portion, a table from the low-latency data may be referred toherein as a data portion, a column from the low-latency data may bereferred to herein as a data portion, a row or record from thelow-latency data may be referred to herein as a data portion, a valuefrom the low-latency data may be referred to herein as a data portion, arelationship defined in the low-latency data may be referred to hereinas a data portion, enterprise ontology data describing the low-latencydata may be referred to herein as a data portion, or any otherdistinctly identifiable data, or combination thereof, from thelow-latency data may be referred to herein as a data portion.

The distributed in-memory database 3300 may create or add one or moredata portions, such as a table, may read from or access one or more dataportions, may update or modify one or more data portions, may remove ordelete one or more data portions, or a combination thereof. Adding,modifying, or removing data portions may include changes to the datamodel of the low-latency data. Changing the data model of thelow-latency data may include notifying one or more other components ofthe low-latency database analysis system 3000, such as by sending, orotherwise making available, a message or signal indicating the change.For example, the distributed in-memory database 3300 may create or add atable to the low-latency data and may transmit or send a message orsignal indicating the change to the semantic interface unit 3600.

In some implementations, a portion of the low-latency data may representa data model of an external enterprise database and may omit the datastored in the external enterprise database, or a portion thereof. Forexample, prioritized data may be cached in the distributed in-memorydatabase 3300 and the other data may be omitted from storage in thedistributed in-memory database 3300, which may be stored in the externalenterprise database. In some implementations, requesting data from thedistributed in-memory database 3300 may include requesting the data, ora portion thereof, from the external enterprise database.

The distributed in-memory database 3300 may receive one or more messagesor signals indicating respective data-queries for the low-latency data,or a portion thereof which may include data-queries for modified,generated, or aggregated data generated based on the low-latency data,or a portion thereof. For example, the distributed in-memory database3300 may receive a data-query from the semantic interface unit 3600,such as in accordance with a request for data. The data-queries receivedby the distributed in-memory database 3300 may be agnostic to thedistributed configuration of the distributed in-memory database 3300. Adata-query, or a portion thereof, may be expressed in accordance withthe defined structured query language implemented by the distributedin-memory database 3300. In some implementations, a data-query may beincluded, such as stored or communicated, in a data-query data structureor container.

The distributed in-memory database 3300 may execute or perform one ormore queries to generate or obtain response data responsive to thedata-query based on the low-latency data.

The distributed in-memory database 3300 may interpret, evaluate, orotherwise process a data-query to generate one or moredistributed-queries, which maybe expressed in accordance with thedefined structured query language. For example, the distributedin-memory database 3300 may identify an in-memory database instance as aquery coordinator may generate a query plan, which may includegenerating one or more distributed-queries, based on the receiveddata-query. The query plan may include query execution instructions forexecuting the received query by the one or more of the in-memorydatabase instances. The query coordinator may distribute, or otherwisemake available, the respective portions of the query executioninstructions to the corresponding in-memory database instances.

The respective in-memory database instances may receive thecorresponding query execution instructions from the query coordinator.The respective in-memory database instances may execute thecorresponding query execution instructions to obtain, process, or both,data. (intermediate results data) from the low-latency data. Therespective in-memory database instances may output, or otherwise makeavailable, the intermediate results data, such as to the querycoordinator.

The query coordinator may execute a respective portion of queryexecution instructions (allocated to the query coordinator) to obtain,process, or both, data (intermediate results data) from the low-latencydata. The query coordinator may receive, or otherwise access, theintermediate results data from the respective in-memory databaseinstances. The query coordinator may combine, aggregate, or otherwiseprocess, the intermediate results data to obtain results data.

In some embodiments, obtaining the intermediate results data by one ormore of the in-memory database instances may include outputting theintermediate results data to, or obtaining intermediate results datafrom, one or more other in-memory database instances, in addition to, orinstead of, obtaining the intermediate results data from the low-latencydata.

The distributed in-memory database 3300 may output, or otherwise makeavailable, the results data to the semantic interface unit 3600.

The enterprise data interface unit 3400 may interface with, orcommunicate with, an external enterprise data system. For example, theenterprise data interface unit 3400 may receive or access enterprisedata from or in an external system, such as an external database. Theenterprise data interface unit 3400 may import, evaluate, or otherwiseprocess the enterprise data to populate, create, or modify data storedin the low-latency database analysis system 3000. The enterprise datainterface unit 3400 may receive, or otherwise access, the enterprisedata from one or more external data sources, such as the external datasource portion 2100 shown in FIG. 2, and may represent the enterprisedata in the low-latency database analysis system 3000 by importing,loading, or populating the enterprise data as principal data in thedistributed in-memory database 3300, such as in one or more low-latencydata structures. The enterprise data interface unit 3400 may implementone or more data connectors, which may transfer data between, forexample, the external data source and the distributed in-memory database3300, which may include altering, formatting, evaluating, ormanipulating the data.

The enterprise data interface unit 3400 may receive, access, or generatemetadata that identifies one or more parameters or relationships for theprincipal data, such as based on the enterprise data, and may includethe generated metadata in the low-latency data stored in the distributedin-memory database 3300. For example, the enterprise data interface unit3400 may identify characteristics of the principal data such as,attributes, measures, values, unique identifiers, tags, links, keys, orthe like, and may include metadata representing the identifiedcharacteristics in the low-latency data stored in the distributedin-memory database 3300. The characteristics of the data can beautomatically determined by receiving, accessing, processing,evaluating, or interpreting the schema in which the enterprise data isstored, which may include automatically identifying links orrelationships between columns, classifying columns (e.g., using columnnames), and analyzing or evaluating the data.

Distinctly identifiable operative data units or structures representingone or more data portions, one or more entities, users, groups, ororganizations represented in the internal data, or one or moreaggregations, collections, relations, analytical results,visualizations, or groupings thereof, may be represented in thelow-latency database analysis system 3000 as objects. An object mayinclude a unique identifier for the object, such as a fully qualifiedname. An object may include a name, such as a displayable value, for theobject.

For example, an object may represent a user, a group, an entity, anorganization, a privilege, a role, a table, a column, a datarelationship, a worksheet, a view, a context, an answer, an insight, apinboard, a tag, a comment, a trigger, a defined variable, a datasource, an object-level security rule, a row-level security rule, or anyother data capable of being distinctly identified and stored orotherwise obtained in the low-latency database analysis system 3000. Anobject may represent or correspond with a logical entity. Datadescribing an object may include data operatively or uniquelyidentifying data corresponding to, or represented by, the object in thelow-latency database analysis system. For example, a column in a tablein a database in the low-latency database analysis system may berepresented in the low-latency database analysis system as an object andthe data describing or defining the object may include data operativelyor uniquely identifying the column.

A worksheet (worksheet object), or worksheet table, may be a logicaltable, or a definition thereof, which may be a collection, a sub-set(such as a subset of columns from one or more tables), or both, of datafrom one or more data sources, such as columns in one or more tables,such as in the distributed in-memory database 3300. A worksheet, or adefinition thereof, may include one or more data organization ormanipulation definitions, such as join paths or worksheet-columndefinitions, which may be user defined. A worksheet may be a datastructure that may contain one or more rules or definitions that maydefine or describe how a respective tabular set of data may be obtained,which may include defining one or more sources of data, such as one ormore columns from the distributed in-memory database 3300. A worksheetmay be a data source. For example, a worksheet may include references toone or more data sources, such as columns in one or more tables, such asin the distributed in-memory database 3300, and a request for datareferencing the worksheet may access the data from the data sourcesreferenced in the worksheet. In some implementations, a worksheet mayomit aggregations of the data from the data sources referenced in theworksheet.

An answer (answer object), or report, may be a defined, such aspreviously generated, request for data such as a resolved-request. Ananswer may include information describing a visualization of dataresponsive to the request for data.

A view (view object) may be a logical table, or a definition thereof,which may be a collection, a sub-set, or both, of data from one or moredata sources, such as columns in one or more tables, such as in thedistributed in-memory database 3300. For example, a view may begenerated based on an answer, such as by storing the answer as a view. Aview may define or describe a data aggregation. A view may be a datasource. For example, a view may include references to one or more datasources, such as columns in one or more tables, such as in thedistributed in-memory database 3300, which may include a definition ordescription of an aggregation of the data from a respective data source,and a request for data referencing the view may access the aggregateddata, the data from the unaggregated data sources referenced in theworksheet, or a combination thereof. The unaggregated data from datasources referenced in the view defined or described as aggregated datain the view may be unavailable based on the view. A view may be amaterialized view or an unmaterialized view. A request for datareferencing a materialized view may obtain data from a set of datapreviously obtained (view-materialization) in accordance with thedefinition of the view and the request for data. A request for datareferencing an unmaterialized view may obtain data from a set of datacurrently obtained in accordance with the definition of the view and therequest for data.

A pinboard (pinboard object), or dashboard, may be a defined collectionor grouping of objects, such as visualizations, answers, or insights.Pinboard data for a pinboard may include information associated with thepinboard, which may be associated with respective objects included inthe pinboard.

A context (context object) may be a set or collection of data associatedwith a request for data or a discretely related sequence or series ofrequests for data or other interactions with the low-latency databaseanalysis system 3000.

A definition may be a set of data describing the structure ororganization of a data portion. For example, in the distributedin-memory database 3300, a column definition may define one or moreaspects of a column in a table, such as a name of the column, adescription of the column, a datatype for the column, or any otherinformation about the column that may be represented as discrete data.

A data source object may represent a source or repository of dataaccessible by the low-latency database analysis system 3000. A datasource object may include data indicating an electronic communicationlocation, such as an address, of a data source, connection information,such as protocol information, authentication information, or acombination thereof, or any other information about the data source thatmay be represented as discrete data. For example, a data source objectmay represent a table in the distributed in-memory database 3300 andinclude data for accessing the table from the database, such asinformation identifying the database, information identifying a schemawithin the database, and information identifying the table within theschema within the database. An external data source object may representan external data source. For example, an external data source object mayinclude data indicating an electronic communication location, such as anaddress, of an external data source, connection information, such asprotocol information, authentication information, or a combinationthereof, or any other information about the external data source thatmay be represented as discrete data.

A sticker (sticker object) may be a description of a classification,category, tag, subject area, or other information that may be associatedwith one or more other objects such that objects associated with asticker may be grouped, sorted, filtered, or otherwise identified basedon the sticker. In the distributed in-memory database 3300 a tag may bea discrete data portion that may be associated with other data portions,such that data portions associated with a tag may be grouped, sorted,filtered, or otherwise identified based on the tag.

The distributed in-memory ontology unit 3500 generates, maintains, orboth, information (ontological data) defining or describing theoperative ontological structure of the objects represented in thelow-latency database analysis system 3000, such as in the low-latencydata stored in the distributed in-memory database 3300, which mayinclude describing attributes, properties, states, or other informationabout respective objects and may include describing relationships amongrespective objects.

Objects may be referred to herein as primary objects, secondary objects,or tertiary objects. Other types of objects may be used.

Primary objects may include objects representing distinctly identifiableoperative data units or structures representing one or more dataportions in the distributed in-memory database 3300, or another datasource in the low-latency database analysis system 3000. For example,primary objects may be data source objects, table objects, columnobjects, relationship objects, or the like. Primary objects may includeworksheets, views, filters, such as row-level-security filters and tablefilters, variables, or the like. Primary objects may be referred toherein as data-objects or queryable-objects.

Secondary objects may be objects representing distinctly identifiableoperative data units or structures representing analytical dataaggregations, collections, analytical results, visualizations, orgroupings thereof, such as pinboard objects, answer objects, insights,visualization objects, and the like. Secondary objects may be referredto herein as analytical-objects.

Tertiary objects may be objects representing distinctly identifiableoperative data units or structures representing operational aspects ofthe low-latency database analysis system 3000, such as one or moreentities, users, groups, or organizations represented in the internaldata, such as user objects, user-group objects, role objects, stickerobjects, and the like.

The distributed in-memory ontology unit 3500 may represent theontological structure, which may include the objects therein, as a graphhaving nodes and edges. A node may be a representation of an object inthe graph structure of the distributed in-memory ontology unit 3500. Anode object can include one or more component objects. Component objectsmay be versioned, such as on a per-component object basis. For example,a node can include a header object, a content object, or both. A headerobject may include information about the node. A content may include thecontent of the node. An edge may represent a relationship between nodes,which may be directional.

In some implementations, the distributed in-memory ontology unit 3500graph may include one or more nodes, edges, or both, representing one ormore objects, relationships or both, corresponding to a respectiveinternal representation of enterprise data stored in an externalenterprise data storage unit, wherein a portion of the data stored inthe external enterprise data storage unit represented in the distributedin-memory ontology unit 3500 graph is omitted from the distributedin-memory database 3300.

In some embodiments, the distributed in-memory ontology unit 3500 maygenerate, modify, or remove a portion of the ontology graph in responseto one or more messages, signals, or notifications from one or more ofthe components of the low-latency database analysis system 3000. Forexample, the distributed in-memory ontology unit 3500 may generate,modify, or remove a portion of the ontology graph in response toreceiving one or more messages, signals, or notifications from thedistributed in-memory database 3300 indicating a change to thelow-latency data structure. In another example, the distributedin-memory database 3300 may send one or more messages, signals, ornotifications indicating a change to the low-latency data structure tothe semantic interface unit 3600 and the semantic interface unit 3600may send one or more messages, signals, or notifications indicating thechange to the low-latency data structure to the distributed in-memoryontology unit 3500.

The distributed in-memory ontology unit 3500 may be distributed,in-memory, multi-versioned, transactional, consistent, durable, or acombination thereof. The distributed in-memory ontology unit 3500 istransactional, which may include implementing atomic concurrent, orsubstantially concurrent, updating of multiple objects. The distributedin-memory ontology unit 3500 is durable, which may include implementinga robust storage that prevents data loss subsequent to or as a result ofthe completion of an atomic operation. The distributed in-memoryontology unit 3500 is consistent, which may include performingoperations associated with a request for data with reference to or usinga discrete data set, which may mitigate or eliminate the riskinconsistent results.

The distributed in-memory ontology unit 3500 may generate, output, orboth, one or more event notifications. For example, the distributedin-memory ontology unit 3500 may generate, output, or both, anotification, or notifications, in response to a change of thedistributed in-memory ontology. The distributed in-memory ontology unit3500 may identify a portion of the distributed in-memory ontology(graph) associated with a change of the distributed in-memory ontology,such as one or more nodes depending from a changed node, and maygenerate, output, or both, a notification, or notifications indicatingthe identified relevant portion of the distributed in-memory ontology(graph). One or more aspects of the low-latency database analysis system3000 may cache object data and may receive the notifications from thedistributed in-memory ontology unit 3500, which may reduce latency andnetwork traffic relative to systems that omit caching object data oromit notifications relevant to changes to portions of the distributedin-memory ontology (graph).

The distributed in-memory ontology unit 3500 may implement prefetching.For example, the distributed in-memory ontology unit 3500 maypredictively, such as based on determined probabilistic utility, fetchone or more nodes, such as in response to access to a related node by acomponent of the low-latency database analysis system 3000.

The distributed in-memory ontology unit 3500 may implement amulti-version concurrency control graph data storage unit. Each node,object, or both, may be versioned. Changes to the distributed in-memoryontology may be reversible. For example, the distributed in-memoryontology may have a first state prior to a change to the distributedin-memory ontology, the distributed in-memory ontology may have a secondstate subsequent to the change, and the state of the distributedin-memory ontology may be reverted to the first state subsequent to thechange, such as in response to the identification of an error or failureassociated with the second state.

In some implementations, reverting a node, or a set of nodes, may omitreverting one or more other nodes. In some implementations, thedistributed in-memory ontology unit 3500 may maintain a change logindicating a sequential record of changes to the distributed in-memoryontology (graph), such that a change to a node or a set of nodes may bereverted and one or more other changes subsequent to the reverted changemay be reverted for consistency.

The distributed in-memory ontology unit 3500 may implement optimisticlocking to reduce lock contention times. The use of optimistic lockingpermits improved throughput of data through the distributed in-memoryontology unit 3500.

The semantic interface unit 3600 may implement procedures and functionsto provide a semantic interface between the distributed in-memorydatabase 3300 and one or more of the other components of the low-latencydatabase analysis system 3000.

The semantic interface unit 3600 may implement ontological datamanagement, data-query generation, authentication and access control,object statistical data collection, or a combination thereof.

Ontological data management may include object lifecycle management,object data persistence, ontological modifications, or the like. Objectlifecycle management may include creating one or more objects, readingor otherwise accessing one or more objects, updating or modifying one ormore objects, deleting or removing one or more objects, or a combinationthereof. For example, the semantic interface unit 3600 may interface orcommunicate with the distributed in-memory ontology unit 3500, which maystore the ontological data, object data, or both, to perform objectlifecycle management, object data persistence, ontologicalmodifications, or the like.

For example, the semantic interface unit 3600 may receive, or otherwiseaccess, a message, signal, or notification, such as from the distributedin-memory database 3300, indicating the creation or addition of a dataportion, such as a table, in the low-latency data stored in thedistributed in-memory database 3300, and the semantic interface unit3600 may communicate with the distributed in-memory ontology unit 3500to create an object in the ontology representing the added data portion.The semantic interface unit 3600 may transmit, send, or otherwise makeavailable, a notification, message, or signal to the relational searchunit 3700 indicating that the ontology has changed.

The semantic interface unit 3600 may receive, or otherwise access, arequest message or signal, such as from the relational search unit 3700,indicating a request for information describing changes to the ontology(ontological updates request). The semantic interface unit 3600 maygenerate and send, or otherwise make available, a response message orsignal to the relational search unit 3700 indicating the changes to theontology (ontological updates response). The semantic interface unit3600 may identify one or more data portions for indexing based on thechanges to the ontology. For example, the changes to the ontology mayinclude adding a table to the ontology, the table including multiplerows, and the semantic interface unit 3600 may identify each row as adata portion for indexing. The semantic interface unit 3600 may includeinformation describing the ontological changes in the ontologicalupdates response. The semantic interface unit 3600 may include one ormore data-query definitions, such as data-query definitions for indexingdata-queries, for each data portion identified for indexing in theontological updates response. For example, the data-query definitionsmay include a sampling data-query, which may be used to query thedistributed in-memory database 3300 for sample data from the added dataportion, an indexing data-query, which may be used to query thedistributed in-memory database 3300 for data from the added dataportion, or both.

The semantic interface unit 3600 may receive, or otherwise access,internal signals or messages including data expressing a usage intent,such as data indicating requests to access or modify the low-latencydata stored in the distributed in-memory database 3300 (e.g., a requestfor data). The request to access or modify the low-latency data receivedby the semantic interface unit 3600 may include a resolved-request. Theresolved-request, which may be database and visualization agnostic, maybe expressed or communicated as an ordered sequence of tokens, which mayrepresent semantic data. For example, the relational search unit 3700may tokenize, identify semantics, or both, based on input data, such asinput data representing user input, to generate the resolved-request.The resolved-request may include an ordered sequence of tokens thatrepresent the request for data corresponding to the input data, and maytransmit, send, or otherwise make accessible, the resolved-request tothe semantic interface unit 3600. The semantic interface unit 3600 mayprocess or respond to a received resolved-request.

The semantic interface unit 3600 may process or transform the receivedresolved-request, which may be, at least in part, incompatible with thedistributed in-memory database 3300, to generate one or morecorresponding data-queries that are compatible with the distributedin-memory database 3300, which may include generating a proto-queryrepresenting the resolved-request, generating a pseudo-queryrepresenting the proto-query, and generating the data-query representingthe pseudo-query.

The semantic interface unit 3600 may generate a proto-query based on theresolved-request. A proto-query, which may be database agnostic, may bestructured or formatted in a form, language, or protocol that differsfrom the defined structured query language of the distributed in-memorydatabase 3300. Generating the proto-query may include identifyingvisualization identification data, such as an indication of a type ofvisualization, associated with the request for data, and generating theproto-query based on the resolved-request and the visualizationidentification data.

The semantic interface unit 3600 may transform the proto-query togenerate a pseudo-query. The pseudo-query, which may be databaseagnostic, may be structured or formatted in a form, language, orprotocol that differs from the defined structured query language of thedistributed in-memory database 3300. Generating a pseudo-query mayinclude applying a defined transformation, or an ordered sequence oftransformations. Generating a pseudo-query may include incorporatingrow-level security filters in the pseudo-query.

The semantic interface unit 3600 may generate a data-query based on thepseudo-query, such as by serializing the pseudo-query. The data-query,or a portion thereof, may be structured or formatted using the definedstructured query language of the distributed in-memory database 3300. Insome implementations, a data-query may be structured or formatted usinga defined structured query language of another database, which maydiffer from the defined structured query language of the distributedin-memory database 3300. Generating the data-query may include using oneor more defined rules for expressing respective the structure andcontent of a pseudo-query in the respective defined structured querylanguage.

The semantic interface unit 3600 may communicate, or issue, thedata-query to the distributed in-memory database 3300. In someimplementations, processing or responding to a resolved-request mayinclude generating and issuing multiple data-query to the distributedin-memory database 3300.

The semantic interface unit 3600 may receive results data from thedistributed in-memory database 3300 responsive to one or moreresolved-requests. The semantic interface unit 3600 may process, format,or transform the results data to obtain visualization data. For example,the semantic interface unit 3600 may identify a visualization forrepresenting or presenting the results data, or a portion thereof, suchas based on the results data or a portion thereof. For example, thesemantic interface unit 3600 may identifying a bar chart visualizationfor results data including one measure and attribute.

Although not shown separately in FIG. 3, the semantic interface unit3600 may include a data visualization unit. In some embodiments, thedata visualization unit may be a distinct unit, separate from thesemantic interface unit 3600. In some embodiments, the datavisualization unit may be included in the system access interface unit3900. The data visualization unit, the system access interface unit3900, or a combination thereof, may generate a user interface, or one ormore portions thereof. For example, data visualization unit, the systemaccess interface unit 3900, or a combination thereof, may obtain theresults data, such as the visualization data, and may generate userinterface elements (visualizations) representing the results data.

The semantic interface unit 3600 may implement object-level security,row-level security, or a combination thereof. Object level security mayinclude security associated with an object, such as a table, a column, aworksheet, an answer, or a pinboard. Row-level security may includeuser-based or group-based access control of rows of data in thelow-latency data, the indexes, or both. The semantic interface unit 3600may implement on or more authentication procedures, access controlprocedures, or a combination thereof.

The semantic interface unit 3600 may implement one or more user-dataintegration features. For example, the semantic interface unit 3600 maygenerate and output a user interface, or a portion thereof, forinputting, uploading, or importing user data, may receive user data, andmay import the user data. For example, the user data may be enterprisedata.

The semantic interface unit 3600 may implement object statistical datacollection. Object statistical data, may include, for respectiveobjects, temporal access information, access frequency information,access recency information, access requester information, or the like.For example, the semantic interface unit 3600 may obtain objectstatistical data as described with respect to the data utility unit3720, the object utility unit 3810, or both. The semantic interface unit3600 may send, transmit, or otherwise make available, the objectstatistical data for data-objects to the data utility unit 3720. Thesemantic interface unit 3600 may send, transmit, or otherwise makeavailable, the object statistical data for analytical-objects to theobject utility unit 3810.

The semantic interface unit 3600 may implement or expose one or moreservices or application programming interfaces. For example, thesemantic interface unit 3600 may, implement one or more services foraccess by the system access interface unit 3900. In someimplementations, one or more services or application programminginterfaces may be exposed to one or more external devices or systems.

The semantic interface unit 3600 may generate and transmit, send, orotherwise communicate, one or more external communications, such ase-mail messages, such as periodically, in response to one or moreevents, or both. For example, the semantic interface unit 3600 maygenerate and transmit, send, or otherwise communicate, one or moreexternal communications including a portable representation, such as aportable document format representation of one or more pinboards inaccordance with a defined schedule, period, or interval. In anotherexample, the semantic interface unit 3600 may generate and transmit,send, or otherwise communicate, one or more external communications inresponse to input data indicating an express request for acommunication. In another example, the semantic interface unit 3600 maygenerate and transmit, send, or otherwise communicate, one or moreexternal communications in response to one or more defined events, suchas the expiration of a recency of access period for a user.

Although shown as a single unit in FIG. 3, the relational search unit3700 may be implemented in a distributed configuration, which mayinclude a primary relational search unit instance and one or moresecondary relational search unit instances.

The relational search unit 3700 may generate, maintain, operate, or acombination thereof, one or more indexes, such as one or more of anontological index, a constituent data index, a control-word index, anumeral index, or a constant index, based on the low-latency data storedin the distributed in-memory database 3300, the low-latency databaseanalysis system 3000, or both. An index may be a defined data structure,or combination of data structures, for storing tokens, terms, or stringkeys, representing a set of data from one or more defined data sourcesin a form optimized for searching. For example, an index may be acollection of index shards. In some implementations, an index may besegmented into index segments and the index segments may be sharded intoindex shards. In some implementations, an index may be partitioned intoindex partitions, the index partitions may be segmented into indexsegments and the index segments may be sharded into index shards.

Generating, or building, an index may be performed to create or populatea previously unavailable index, which may be referred to as indexing thecorresponding data, and may include regenerating, rebuilding, orreindexing to update or modify a previously available index, such as inresponse to a change in the indexed data (constituent data).

The ontological index may be an index of data (ontological data)describing the ontological structure or schema of the low-latencydatabase analysis system 3000, the low-latency data stored in thedistributed in-memory database 3300, or a combination thereof. Forexample, the ontological index may include data representing the tableand column structure of the distributed in-memory database 3300. Therelational search unit 3700 may generate, maintain, or both, theontological index by communicating with, such as requesting ontologicaldata from, the distributed in-memory ontology unit 3500, the semanticinterface unit 3600, or both, Each record in the ontological index maycorrespond to a respective ontological token, such as a token thatidentifies a column by name.

The control-word index may be an index of a defined set of control-wordtokens. A control-word token may be a character, a symbol, a word, or adefined ordered sequence of characters or symbols, that is identified inone or more grammars of the low-latency database analysis system 3000 ashaving one or more defined grammatical functions, which may becontextual. For example, the control-word index may include thecontrol-word token “sum”, which may be identified in one or moregrammars of the low-latency database analysis system 3000 as indicatingan additive aggregation. In another example, the control-word index mayinclude the control-word token “top”, which may be identified in one ormore grammars of the low-latency database analysis system 3000 asindicating a maximal value from an ordered set. In another example, thecontrol-word index may include operator tokens, such as the equalityoperator token (“=”). The constant index may be an index of constanttokens such as “100” or “true”. The numeral index may be an index ofnumber word tokens (or named numbers), such as number word tokens forthe positive integers between zero and one million, inclusive. Forexample, “one hundred and twenty eight”.

A token may be a word, phrase, character, sequence of characters,symbol, combination of symbols, or the like. A token may represent adata portion in the low-latency data stored in the low-latency datastructure. For example, the relational search unit 3700 mayautomatically generate respective tokens representing the attributes,the measures, the tables, the columns, the values, unique identifiers,tags, links, keys, or any other data portion, or combination of dataportions, or a portion thereof. The relational search unit 3700 mayclassify the tokens, which may include storing token classification datain association with the tokens. For example, a token may be classifiedas an attribute token, a measure token, a value token, or the like.

The constituent data index may be an index of the constituent datavalues stored in the low-latency database analysis system 3000, such asin the distributed in-memory database 3300. The relational search unit3700 may generate, maintain, or both, the constituent data index bycommunicating with, such as requesting data from, the distributedin-memory database 3300. For example, the relational search unit 3700may send, or otherwise communicate, a message or signal to thedistributed in-memory database 3300 indicating a request to perform anindexing data-query, the relational search unit 3700 may receiveresponse data from the distributed in-memory database 3300 in responseto the requested indexing data-query, and the relational search unit3700 may generate the constituent data index, or a portion thereof,based on the response data. For example, the constituent data index mayindex data-objects.

An index shard may be used for token searching, such as exact matchsearching; prefix match searching, substring match searching, or suffixmatch searching. Exact match searching may include identifying tokens inthe index shard that matches a defined target value. Prefix matchsearching may include identifying tokens in the index shard that includea prefix, or begin with a value, such as a character or string, thatmatches a defined target value. Substring match searching may includeidentifying tokens in the index shard that include a value, such as acharacter or string, that matches a defined target value. Suffix matchsearching may include identifying tokens in the index shard that includea suffix, or end with a value, such as a character or string, thatmatches a defined target value. In some implementations, an index shardmay include multiple distinct index data structures. For example, anindex shard may include a first index data structure optimized for exactmatch searching, prefix match searching, and suffix match searching, anda second index data structure optimized for substring match searching.Traversing, or otherwise accessing, managing, or using, an index mayinclude identifying one or more of the index shards of the index andtraversing the respective index shards. In some implementations, one ormore indexes, or index shards, may be distributed, such as replicated onmultiple relational search unit instances. For example, the ontologicalindex may be replicated on each relational search unit instance.

The relational search unit 3700 may receive a request for data from thelow-latency database analysis system 3000. For example, the relationalsearch unit 3700 may receive data expressing a usage intent indicatingthe request for data in response to input, such as user input, obtainedvia a user interface, such as a user interface generated, or partiallygenerated, by the system access interface unit 3900, which may be a userinterface operated on an external device, such as one of the clientdevices 2320, 2340 shown in FIG. 2. In some implementations, therelational search unit 3700 may receive the data expressing the usageintent from the system access interface unit 3900 or from the semanticinterface unit 3600. For example, the relational search unit 3700 mayreceive or access the data expressing the usage intent in a request fordata message or signal.

The relational search unit 3700 may process, parse, identify semantics,tokenize, or a combination thereof, the request for data to generate aresolved-request, which may include identifying a database andvisualization agnostic ordered sequence of tokens based on the dataexpressing the usage intent. The data expressing the usage intent, orrequest for data, may include request data, such as resolved requestdata, unresolved request data, or a combination of resolved request dataand unresolved request data. The relational search unit 3700 mayidentify the resolved request data. The relational search unit 3700 mayidentify the unresolved request data and may tokenize the unresolvedrequest data.

Resolved request data may be request data identified in the dataexpressing the usage intent as resolved request data. Each resolvedrequest data portion may correspond with a respective token in thelow-latency database analysis system 3000. The data expressing the usageintent may include information identifying one or more portions of therequest data as resolved request data.

Unresolved request data may be request data identified in the dataexpressing the usage intent as unresolved request data, or request datafor which the data expressing the usage intent omits informationidentifying the request data a resolved request data. Unresolved requestdata may include text or sting data, which may include a character,sequence of characters, symbol, combination of symbols, word, sequenceof words, phrase, or the like, for which information, such astokenization binding data, identifying the text or string data asresolved request data is absent or omitted from the request data. Thedata expressing the usage intent may include information identifying oneor more portions of the request data as unresolved request data. Thedata expressing the usage intent may omit information identifyingwhether one or more portions of the request data are resolved requestdata. The relational search unit 3700 may identify one or more portionsof the request data for which the data expressing the usage intent omitsinformation identifying whether the one or more portions of the requestdata are resolved request data as unresolved request data.

For example, the data expressing the usage intent may include a requeststring and one or more indications that one or more portions of therequest string are resolved request data. One or more portions of therequest string that are not identified as resolved request data in thedata expressing the usage intent may be identified as unresolved requestdata. For example, the data expressing the usage intent may include therequest string “example text”; the data expressing the usage intent mayinclude information indicating that the first portion of the requeststring, “example”, is resolved request data; and the data expressing theusage intent may omit information indicating that the second portion ofthe request string, “text”, is resolved request data.

The information identifying one or more portions of the request data asresolved request data may include tokenization binding data indicating apreviously identified token corresponding to the respective portion ofthe request data. The tokenization binding data corresponding to arespective token may include, for example, one or more of a columnidentifier indicating a column corresponding to the respective token, adata type identifier corresponding to the respective token, a tableidentifier indicating a table corresponding to the respective token; anindication of an aggregation corresponding to the respective token, oran indication of a join path associated with the respective token. Othertokenization binding data may be used. In some implementations, the dataexpressing the usage intent may omit the tokenization binding data andmay include an identifier that identifies the tokenization binding data.

The relational search unit 3700 may implement or access one or moregrammar-specific tokenizers, such as a tokenizer for a definedrelational search grammar or a tokenizer for a natural-language grammar.For example, the relational search unit 3700 may implement one or moreof a formula tokenizer, a row-level-security tokenizer, a relationalsearch tokenizer, or a natural language tokenizer. Other tokenizers maybe used. In some implementations, the relational search unit 3700 mayimplement one or more of the grammar-specific tokenizers, or a portionthereof, by accessing another component of the low-latency databaseanalysis system 3000 that implements the respective grammar-specifictokenizer, or a portion thereof. For example, the natural languageprocessing unit 3710 may implement the natural language tokenizer andthe relational search unit 3700 may access the natural languageprocessing unit 3710 to implement natural language tokenization.

A tokenizer, such as the relational search tokenizer, may parse text orstring data (request string), such as string data included in a dataexpressing the usage intent, in a defined read order, such as from leftto right, such as on a character-by-character or symbol-by-symbol basis.For example, a request string may include a single character, symbol, orletter, and tokenization may include identifying one or more tokensmatching, or partially matching, the input character.

Tokenization may include parsing the request string to identify one ormore words or phrases. For example, the request string may include asequence of characters, symbols, or letters, and tokenization mayinclude parsing the sequence of characters in a defined order, such asfrom left to right, to identify distinct words or terms and identifyingone or more tokens matching the respective words. In someimplementations, word or phrase parsing may be based on one or more of aset of defined delimiters, such as a whitespace character, a punctuationcharacter, or a mathematical operator.

The relational search unit 3700 may traverse one or more of the indexesto identify one or more tokens corresponding to a character, word, orphrase identified in request string. Tokenization may includeidentifying multiple candidate tokens matching a character, word, orphrase identified in request string. Candidate tokens may be ranked orordered, such as based on probabilistic utility.

Tokenization may include match-length maximization. Match-lengthmaximization may include ranking or ordering candidate matching tokensin descending magnitude order. For example, the longest candidate token,having the largest cardinality of characters or symbols, matching therequest string, or a portion thereof, may be the highest rankedcandidate token. For example, the request string may include a sequenceof words or a semantic phrase, and tokenization may include identifyingone or more tokens matching the input semantic phrase. In anotherexample, the request string may include a sequence of phrases, andtokenization may include identifying one or more tokens matching theinput word sequence. In some implementations, tokenization may includeidentifying the highest ranked candidate token for a portion of therequest string as a resolved token for the portion of the requeststring.

The relational search unit 3700 may implement one or more finite statemachines. For example, tokenization may include using one or more finitestate machines. A finite state machine may model or represent a definedset of states and a defined set of transitions between the states. Astate may represent a condition of the system represented by the finitestate machine at a defined temporal point. A finite state machine maytransition from a state (current state) to a subsequent state inresponse to input (e.g., input to the finite state machine). Atransition may define one or more actions or operations that therelational search unit 3700 may implement. One or more of the finitestate machines may be non-deterministic, such that the finite statemachine may transition from a state to zero or more subsequent states.

The relational search unit 3700 may generate, instantiate, or operate atokenization finite state machine, which may represent the respectivetokenization grammar. Generating, instantiating, or operating a finitestate machine may include operating a finite state machine traverser fortraversing the finite state machine. Instantiating the tokenizationfinite state machine may include entering an empty state, indicating theabsence of received input. The relational search unit 3700 may initiateor execute an operation, such as an entry operation, corresponding tothe empty state in response to entering the empty state. Subsequently,the relational search unit 3700 may receive input data, and thetokenization finite state machine may transition from the empty state toa state corresponding to the received input data. In some embodiments,the relational search unit 3700 may initiate one or more data-queries inresponse to transitioning to or from a respective state of a finitestate machine. In the tokenization finite state machine, a state mayrepresent a possible next token in the request string. The tokenizationfinite state machine may transition between states based on one or moredefined transition weights, which may indicate a probability oftransiting from a state to a subsequent state.

The tokenization finite state machine may determine tokenization basedon probabilistic path utility. Probabilistic path utility may rank ororder multiple candidate traversal paths for traversing the tokenizationfinite state machine based on the request string. The candidate pathsmay be ranked or ordered based on one or more defined probabilistic pathutility metrics, which may be evaluated in a defined sequence. Forexample, the tokenization finite state machine may determineprobabilistic path utility by evaluating the weights of the respectivecandidate transition paths, the lengths of the respective candidatetransition paths, or a combination thereof. In some implementations, theweights of the respective candidate transition paths may be evaluatedwith high priority relative to the lengths of the respective candidatetransition paths.

In some implementations, one or more transition paths evaluated by thetokenization finite state machine may include a bound state such thatthe candidate tokens available for tokenization of a portion of therequest string may be limited based on the tokenization of a previouslytokenized portion of the request string.

Tokenization may include matching a portion of the request string to oneor more token types, such as a constant token type, a column name tokentype, a value token type, a control-word token type, a date value tokentype, a string value token type, or any other token type defined by thelow-latency database analysis system 3000. A constant token type may bea fixed, or invariant, token type, such as a numeric value. A columnname token type may correspond with a name of a column in the datamodel. A value token type may correspond with an indexed data value. Acontrol-word token type may correspond with a defined set ofcontrol-words. A date value token type may be similar to a control-wordtoken type and may correspond with a defined set of control-words fordescribing temporal information. A string value token type maycorrespond with an unindexed value.

Token matching may include ordering or weighting candidate token matchesbased on one or more token matching metrics. Token matching metrics mayinclude whether a candidate match is within a defined data scope, suchas a defined set of tables, wherein a candidate match outside thedefined data scope (out-of-scope) may be ordered or weighted lower thana candidate match within the define data scope (in-scope). Tokenmatching metrics may include whether, or the degree to which, acandidate match increases query complexity, such as by spanning multipleroots, wherein a candidate match that increases complexity may beordered or weighted lower than a candidate match that does not increasecomplexity or increases complexity to a lesser extent. Token matchingmetrics may include whether the candidate match is an exact match or apartial match, wherein a candidate match that is a partial may beordered or weighted lower than a candidate match that is an exact match.In some implementations, the cardinality of the set of partial matchesmay be limited to a defined value.

Token matching metrics may include a token score (TokenScore), wherein acandidate match with a relatively low token score may be ordered orweighted lower than a candidate match with a relatively high tokenscore. The token score for a candidate match may be determined based oneor more token scoring metrics. The token scoring metrics may include afinite state machine transition weight metric (FSMScore), wherein aweight of transitioning from a current state of the tokenization finitestate machine to a state indicating a candidate matching token is thefinite state machine transition weight metric. The token scoring metricsmay include a cardinality penalty metric (CardinalityScore), wherein acardinality of values (e.g., unique values) corresponding to thecandidate matching token is used as a penalty metric (inversecardinality), which may reduce the token score. The token scoringmetrics may include an index utility metric (Index Score), wherein adefined utility value, such as one, associated with an object, such as acolumn wherein the matching token represents the column or a value fromthe column, is the index utility metric. In some implementations, thedefined utility values may be configured, such as in response to userinput, on a per object (e.g., per column) basis. The token scoringmetrics may include a usage metric (UBRScore). The usage metric may bedetermined based on a usage based ranking index, one or more usageranking metrics, or a combination thereof. Determining the usage metric(UBRScore) may include determining a usage boost value (UBRBoost). Thetoken score may be determined based on a defined combination of tokenscoring metrics. For example, determining the token score may beexpressed as the following:

TokenScore=FSMScore*(IndexScore+UBRScore*UBRBoost)+Min(CardinalityScore, 1).

Token matching may include grouping candidate token matches by matchtype, ranking or ordering on a per-match type basis based on tokenscore, and ranking or ordering the match types. For example, the matchtypes may include a first match type for exact matches (having thehighest match type priority order), a second match type for prefixmatches on ontological data (having a match type priority order lowerthan the first match type), a third match type for substring matches onontological data and prefix matches on data values (having a match typepriority order lower than the second match type), a fourth match typefor substring matches on data values (having a match type priority orderlower than the third match type), and a fifth match type for matchesomitted from the first through fourth match types (having a match typepriority order lower than the fourth match type). Other match types andmatch type orders may be used.

Tokenization may include ambiguity resolution. Ambiguity resolution mayinclude token ambiguity resolution, join-path ambiguity resolution, orboth. In some implementations, ambiguity resolution may ceasetokenization in response to the identification of an automatic ambiguityresolution error or failure.

Token ambiguity may correspond with identifying two or more exactlymatching candidate matching tokens. Token ambiguity resolution may bebased on one or more token ambiguity resolution metrics. The tokenambiguity resolution metrics may include using available previouslyresolved token matching or binding data and token ambiguity may beresolved in favor of available previously resolved token matching orbinding data, other relevant tokens resolved from the request string, orboth. The token ambiguity resolution may include resolving tokenambiguity in favor of integer constants. The token ambiguity resolutionmay include resolving token ambiguity in favor of control-words, such asfor tokens at the end of a request for data, such as last, that are notbeing edited.

Join-path ambiguity may correspond with identifying matching tokenshaving two or more candidate join paths. Join-path ambiguity resolutionmay be based on one or more join-path ambiguity resolution metrics. Thejoin-path ambiguity resolution metrics may include using availablepreviously resolved join-path binding data and join-path ambiguity maybe resolved in favor of available previously resolved join-paths. Thejoin-path ambiguity resolution may include favoring join paths thatinclude in-scope objects over join paths that include out-of-scopeobjects. The join-path ambiguity resolution metrics may include acomplexity minimization metric, which may favor a join path that omitsor avoids increasing complexity over join paths that increasecomplexity, such as a join path that may introduce a chasm trap.

The relational search unit 3700 may identify a resolved-request based onthe request string. The resolved-request, which may be database andvisualization agnostic, may be expressed or communicated as an orderedsequence of tokens representing the request for data indicated by therequest string. The relational search unit 3700 may instantiate, orgenerate, one or more resolved-request objects. For example, therelational search unit 3700 may create or store a resolved-requestobject corresponding to the resolved-request in the distributedin-memory ontology unit 3500. The relational search unit 3700 maytransmit, send, or otherwise make available, the resolved-request to thesemantic interface unit 3600.

In some implementations, the relational search unit 3700 may transmit,send, or otherwise make available, one or more resolved-requests, orportions thereof, to the semantic interface unit 3600 in response tofinite state machine transitions. For example, the relational searchunit 3700 may instantiate a search object in response to a firsttransition of a finite state machine. The relational search unit 3700may include a first search object instruction in the search object inresponse to a second transition of the finite state machine. Therelational search unit 3700 may send the search object including thefirst search object instruction to the semantic interface unit 3600 inresponse to the second transition of the finite state machine. Therelational search unit 3700 may include a second search objectinstruction in the search object in response to a third transition ofthe finite state machine. The relational search unit 3700 may send thesearch object including the search object instruction, or a combinationof the first search object instruction and the second search objectinstruction, to the semantic interface unit 3600 in response to thethird transition of the finite state machine. The search objectinstructions may be represented using any annotation, instruction, text,message, list, pseudo-code, comment, or the like, or any combinationthereof that may be converted, transcoded, or translated into structuredsearch instructions for retrieving data from the low-latency data.

The relational search unit 3700 may provide an interface to permit thecreation of user-defined syntax. For example, a user may associate astring with one or more tokens. Accordingly, when the string is entered,the pre-associated tokens are returned in lieu of searching for tokensto match the input.

The relational search unit 3700 may include a localization unit (notexpressly shown). The localization, globalization, regionalization, orinternationalization, unit may obtain source data expressed inaccordance with a source expressive-form and may output destination datarepresenting the source data, or a portion thereof, and expressed usinga destination expressive-form. The data expressive-forms, such as thesource expressive-form and the destination expressive-form, may includeregional or customary forms of expression, such as numeric expression,temporal expression, currency expression, alphabets, natural-languageelements, measurements, or the like. For example, the sourceexpressive-form may be expressed using a canonical-form, which mayinclude using a natural-language, which may be based on English, and thedestination expressive-form may be expressed using a locale-specificform, which may include using another natural-language, which may be anatural-language that differs from the canonical-language. In anotherexample, the destination expressive-form and the source expressive-formmay be locale-specific expressive-forms and outputting the destinationexpressive-form representation of the source expressive-form data mayinclude obtaining a canonical-form representation of the sourceexpressive-form data and obtaining the destination expressive-formrepresentation based on the canonical-form representation. Although, forsimplicity and clarity, the grammars described herein, such as therelational search grammar and the natural language search grammar, aredescribed with relation to the canonical expressive-form, theimplementation of the respective grammars, or portions thereof,described herein may implement locale-specific expressive-forms. Forexample, the relational search tokenizer may include multiplelocale-specific relational search tokenizers.

The natural language processing unit 3710 may receive input dataincluding a natural language string, such as a natural language stringgenerated in accordance with user input. The natural language string mayrepresent a data request expressed in an unrestricted natural languageform, for which data identified or obtained prior to, or in conjunctionwith, receiving the natural language string by the natural languageprocessing unit 3710 indicating the semantic structure, correlation tothe low-latency database analysis system 3000, or both, for at least aportion of the natural language string is unavailable or incomplete.Although not shown separately in FIG. 3, in some implementations, thenatural language string may be generated or determined based onprocessing an analog signal, or a digital representation thereof, suchas an audio stream or recording or a video stream or recording, whichmay include using speech-to-text conversion.

The natural language processing unit 3710 may analyze, process, orevaluate the natural language string, or a portion thereof, to generateor determine the semantic structure, correlation to the low-latencydatabase analysis system 3000, or both, for at least a portion of thenatural language string. For example, the natural language processingunit 3710 may identify one or more words or terms in the naturallanguage string and may correlate the identified words to tokens definedin the low-latency database analysis system 3000. In another example,the natural language processing unit 3710 may identify a semanticstructure for the natural language string, or a portion thereof inanother example, the natural language processing unit 3710 may identifya probabilistic intent for the natural language string, or a portionthereof, which may correspond to an operative feature of the low-latencydatabase analysis system 3000, such as retrieving data from the internaldata, analyzing data the internal data, or modifying the internal data.

The natural language processing unit 3710 may send, transmit, orotherwise communicate request data indicating the tokens, relationships,semantic data, probabilistic intent, or a combination thereof or one ormore portions thereof, identified based on a natural language string tothe relational search unit 3700.

The data utility unit 3720 may receive, process, and maintainuser-agnostic utility data, such as system configuration data;user-specific utility data, such as utilization data, or bothuser-agnostic and user-specific utility data. The utility data mayindicate whether a data portion, such as a column, a record, an insight,or any other data portion, has high utility or low utility within thesystem, such across all users of the system. For example, the utilitydata may indicate that a defined column is a high-utility column or alow-utility column. The data utility unit 3720 may store the utilitydata, such as using the low-latency data structure. For example, inresponse to a user using, or accessing, a data portion, data utilityunit 3720 may store utility data indicating the usage, or access, eventfor the data portion, which may include incrementing a usage eventcounter associated with the data portion. In some embodiments, the datautility unit 3720 may receive the information indicating the usage, oraccess, event for the data portion from the insight unit 3730, and theusage, or access, event for the data portion may indicate that the usageis associated with an insight.

The data utility unit 3720 may receive a signal, message, or othercommunication, indicating a request for utility information. The requestfor utility information may indicate an object or data portion. The datautility unit 3720 may determine, identify, or obtain utility dataassociated with the identified object or data portion. The data utilityunit 3720 may generate and send utility response data responsive to therequest that may indicate the utility data associated with theidentified object or data portion.

The data utility unit 3720 may generate, maintain, operate, or acombination thereof, one or more indexes, such as one or more of a usage(or utility) index, a resolved-request index, or a phrase index, basedon the low-latency data stored in the distributed in-memory database3300, the low-latency database analysis system 3000, or both.

The insight unit 3730 may automatically identify one or more insights,which may be data other than data expressly requested by a user, andwhich may be identified and prioritized, or both, based on probabilisticutility.

The object search unit 3800 may generate, maintain, operate, or acombination thereof, one or more object-indexes, which may be based onthe analytical-objects represented in the low-latency database analysissystem 3000, or a portion thereof, such as pinboards, answers, andworksheets. An object-index may be a defined data structure, orcombination of data structures, for storing analytical-object data in aform optimized for searching. Although shown as a single unit in FIG. 3,the object search unit 3800 may interface with a distinct, separate,object indexing unit (not expressly shown).

The object search unit 3800 may include an object-index populationinterface, an object-index search interface, or both. The object-indexpopulation interface may obtain and store, load, or populateanalytical-object data, or a portion thereof, in the object-indexes. Theobject-index search interface may efficiently access or retrieveanalytical-object data from the object-indexes such as by searching ortraversing the object-indexes, or one or more portions thereof. In someimplementations, the object-index population interface, or a portionthereof, may be a distinct, independent unit.

The object-index population interface may populate, update, or both theobject-indexes, such as periodically, such as in accordance with adefined temporal period, such as thirty minutes. Populating, orupdating, the object-indexes may include obtaining object indexing datafor indexing the analytical-objects represented in the low-latencydatabase analysis system 3000. For example, the object-index populationinterface may obtain the analytical-object indexing data, such as fromthe distributed in-memory ontology unit 3500. Populating, or updating,the object-indexes may include generating or creating an indexing datastructure representing an object. The indexing data structure forrepresenting an object may differ from the data structure used forrepresenting the object in other components of the low-latency databaseanalysis system 3000, such as in the distributed in-memory ontology unit3500.

The object indexing data for an analytical-object may be a subset of theobject data for the analytical-object. The object indexing data for ananalytical-object may include an object identifier for theanalytical-object uniquely identifying the analytical-object in thelow-latency database analysis system 3000, or in a defined data-domainwithin the low-latency database analysis system 3000. The low-latencydatabase analysis system 3000 may uniquely, unambiguously, distinguishan object from other objects based on the object identifier associatedwith the object. The object indexing data for an analytical-object mayinclude data non-uniquely identifying the object. The low-latencydatabase analysis system 3000 may identify one or moreanalytical-objects based on the non-uniquely identifying data associatedwith the respective objects, or one or more portions thereof. In someimplementations, an object identifier may be an ordered combination ofnon-uniquely identifying object data that, as expressed in the orderedcombination, is uniquely identifying. The low-latency database analysissystem 3000 may enforce the uniqueness of the object identifiers.

Populating, or updating, the object-indexes may include indexing theanalytical-object by including or storing the object indexing data inthe object-indexes. For example, the object indexing data may includedata for an analytical-object, the object-indexes may omit data for theanalytical-object, and the object-index population interface may includeor store the object indexing data in an object-index. In anotherexample, the object indexing data may include data for ananalytical-object, the object-indexes may include data for theanalytical-object, and the object-index population interface may updatethe object indexing data for the analytical-object in the object-indexesin accordance with the object indexing data.

Populating, or updating, the object-indexes may include obtaining objectutility data for the analytical-objects represented in the low-latencydatabase analysis system 3000. For example, the object-index populationinterface may obtain the object utility data, such as from the objectutility unit 3810. The object-index population interface may include theobject utility data in the object-indexes in association with thecorresponding objects.

In some implementations, the object-index population interface mayreceive, obtain, or otherwise access the object utility data from adistinct, independent, object utility data population unit, which mayread, obtain, or otherwise access object utility data from the objectutility unit 3810 and may send, transmit, or otherwise provide, theobject utility data to the object search unit 3800. The object utilitydata population unit may send, transmit, or otherwise provide, theobject utility data to the object search unit 3800 periodically, such asin accordance with a defined temporal period, such as thirty minutes.

The object-index search interface may receive, access, or otherwiseobtain data expressing a usage intent with respect to the low-latencydatabase analysis system 3000, which may represent a request to accessdata in the low-latency database analysis system 3000, which mayrepresent a request to access one or more analytical-objects representedin the low-latency database analysis system 3000. The object-indexsearch interface may generate one or more object-index queries based onthe data expressing the usage intent. The object-index search interfacemay send, transmit, or otherwise make available the object-index queriesto one or more of the object-indexes.

The object-index search interface may receive, obtain, or otherwiseaccess object search results data indicating one or moreanalytical-objects identified by searching or traversing theobject-indexes in accordance with the object-index queries. Theobject-index search interface may sort or rank the object search resultsdata based on probabilistic utility in accordance with the objectutility data for the analytical-objects in the object search resultsdata. In some implementations, the object-index search interface mayinclude one or more object search ranking metrics with the object-indexqueries and may receive the object search results data sorted or rankedbased on probabilistic utility in accordance with the object utilitydata for the objects in the object search results data and in accordancewith the object search ranking metrics.

For example, the data expressing the usage intent may include a useridentifier, and the object search results data may include object searchresults data sorted or ranked based on probabilistic utility for theuser. In another example, the data expressing the usage intent mayinclude a user identifier and one or more search terms, and the objectsearch results data may include object search results data sorted orranked based on probabilistic utility for the user identified bysearching or traversing the object-indexes in accordance with the searchterms.

The object-index search interface may generate and send, transmit, orotherwise make available the sorted or ranked object search results datato another component of the low-latency database analysis system 3000,such as for further processing and display to the user.

The object utility unit 3810 may receive, process, and maintainuser-specific object utility data for objects represented in thelow-latency database analysis system 3000. The user-specific objectutility data may indicate whether an object has high utility or lowutility for the user.

The object utility unit 3810 may store the user-specific object utilitydata, such as on a per-object basis, a per-activity basis, or both. Forexample, in response to data indicating an object access activity, suchas a user using, viewing, or otherwise accessing, an object, the objectutility unit 3810 may store user-specific object utility data indicatingthe object access activity for the object, which may includeincrementing an object access activity counter associated with theobject, which may be a user-specific object access activity counter. Inanother example, in response to data indicating an object storageactivity, such as a user storing an object, the object utility unit 3810may store user-specific object utility data indicating the objectstorage activity for the object, which may include incrementing astorage activity counter associated with the object, which may be auser-specific object storage activity counter. The user-specific objectutility data may include temporal information, such as a temporallocation identifier associated with the object activity. Otherinformation associated with the object activity may be included in theobject utility data.

The object utility unit 3810 may receive a signal, message, or othercommunication, indicating a request for object utility information. Therequest for object utility information may indicate one or more objects,one or more users, one or more activities, temporal information, or acombination thereof. The request for object utility information mayindicate a request for object utility data, object utility counter data,or both.

The object utility unit 3810 may determine, identify, or obtain objectutility data in accordance with the request for object utilityinformation. The object utility unit 3810 may generate and send objectutility response data responsive to the request that may indicate theobject utility data, or a portion thereof, in accordance with therequest for object utility information.

For example, a request for object utility information may indicate auser, an object, temporal information, such as information indicating atemporal span, and an object activity, such as the object accessactivity. The request for object utility information may indicate arequest for object utility counter data. The object utility unit 3810may determine, identify, or obtain object utility counter dataassociated with the user, the object, and the object activity having atemporal location within the temporal span, and the object utility unit3810 may generate and send object utility response data including theidentified object utility counter data.

In some implementations, a request for object utility information mayindicate multiple users, or may omit indicating a user, and the objectutility unit 3810 may identify user-agnostic object utility dataaggregating the user-specific object utility data. In someimplementations, a request for object utility information may indicatemultiple objects, may omit indicating an object, or may indicate anobject type, such as answer, pinboard, or worksheet, and the objectutility unit 3810 may identify the object utility data by aggregatingthe Object utility data for multiple objects in accordance with therequest. Other object utility aggregations may be used.

The system configuration unit 3820 implement or apply one or morelow-latency database analysis system configurations to enable, disable,or configure one or more operative features of the low-latency databaseanalysis system 3000. The system configuration unit 3820 may store datarepresenting or defining the one or more low-latency database analysissystem configurations. The system configuration unit 3820 may receivesignals or messages indicating input data, such as input data generatedvia a system access interface, such as a user interface, for accessingor modifying the low-latency database analysis system configurations.The system configuration unit 3820 may generate, modify, delete, orotherwise maintain the low-latency database analysis systemconfigurations, such as in response to the input data. The systemconfiguration unit 3820 may generate or determine output data, and mayoutput the output data, for a system access interface, or a portion orportions thereof, for the low-latency database analysis systemconfigurations, such as for presenting a user interface for thelow-latency database analysis system configurations. Although not shownin FIG. 3, the system configuration unit 3820 may communicate with arepository, such as an external centralized repository, of low-latencydatabase analysis system configurations; the system configuration unit3820 may receive one or more low-latency database analysis systemconfigurations from the repository, and may control or configure one ormore operative features of the low-latency database analysis system 3000in response to receiving one or more low-latency database analysissystem configurations from the repository.

The user customization unit 3830 may receive, process, and maintainuser-specific utility data, such as user defined configuration data,user defined preference data, or a combination thereof. Theuser-specific utility data may indicate whether a data portion, such asa column, a record, an insight, or any other data portion or object, hashigh utility or low utility to an identified user. For example, theuser-specific utility data may indicate that a defined column is ahigh-utility column or a low-utility column. The user customization unit3830 may store the user-specific utility data, such as using thelow-latency data structure. The user customization unit 3830 may storethe feedback at an individual level and may include the context in whichfeedback was received from the user. Feedback may be stored in adisk-based system. In some implementations, feedback may be stored in anin-memory storage.

The system access interface unit 3900 may interface with, or communicatewith, a system access unit (not shown in FIG. 3), which may be a clientdevice, a user device, or another external device or system, or acombination thereof, to provide access to the internal data; features ofthe low-latency database analysis system 3000, or a combination thereof.For example, the system access interface unit 3900 may receive signals,message, or other communications representing interactions with theinternal data, such as data expressing a usage intent and may outputresponse messages, signals, or other communications responsive to thereceived requests.

The system access interface unit 3900 may generate data for presenting auser interface, or one or more portions thereof, for the low-latencydatabase analysis system 3000. For example, the system access interfaceunit 3900 may generate instructions for rendering, or otherwisepresenting, the user interface, or one or more portions thereof and maytransmit, or otherwise make available, the instructions for rendering,or otherwise presenting, the user interface, or one or more portionsthereof to the system access unit, for presentation to a user of thesystem access unit. For example, the system access unit may present theuser interface via a web browser or a web application and theinstructions may be in the form of HTML, JavaScript, or the like.

In an example, the system access interface unit 3900 may include asearch field user interface element in the user interface. The searchfield user interface element may be an unstructured search string userinput element or field. The system access unit may display theunstructured search string user input element. The system access unitmay receive input data, such as user input data, corresponding to theunstructured search string user input element. The system access unitmay transmit, or otherwise make available, the unstructured searchstring user input to the system access interface unit 3900. The userinterface may include other user interface elements and the systemaccess unit may transmit, or otherwise make available, other user inputdata to the system access interface unit 3900.

The system access interface unit 3900 may obtain the user input data,such as the unstructured search string, from the system access unit. Thesystem access interface unit 3900 may transmit, or otherwise makeavailable, the user input data to one or more of the other components ofthe low-latency database analysis system 3000.

In some embodiments, the system access interface unit 3900 may obtainthe unstructured search string user input as a sequence of individualcharacters or symbols, and the system access interface unit 3900 maysequentially transmit, or otherwise make available, individual or groupsof characters or symbols of the user input data to one or more of theother components of the low-latency database analysis system 3000.

In some embodiments, system access interface unit 3900 may obtain theunstructured search string user input may as a sequence of individualcharacters or symbols, the system access interface unit 3900 mayaggregate the sequence of individual characters or symbols, and maysequentially transmit, or otherwise make available, a currentaggregation of the received user input data to one or more of the othercomponents of the low-latency database analysis system 3000, in responseto receiving respective characters or symbols from the sequence, such ason a per-character or per-symbol basis.

The real-time collaboration unit 3910 may receive signals or messagesrepresenting input received in accordance with multiple users, ormultiple system access devices, associated with a collaboration contextor session, may output data, such as visualizations, generated ordetermined by the low-latency database analysis system 3000 to multipleusers associated with the collaboration context or session, or both. Thereal-time collaboration unit 3910 may receive signals or messagesrepresenting input received in accordance with one or more usersindicating a request to establish a collaboration context or session,and may generate, maintain, or modify collaboration data representingthe collaboration context or session, such as a collaboration sessionidentifier. The real-time collaboration unit 3910 may receive signals ormessages representing input received in accordance with one or moreusers indicating a request to participate in, or otherwise associatewith, a currently active collaboration context or session, and mayassociate the one or more users with the currently active collaborationcontext or session. In some implementations, the input, output, or both,of the real-time collaboration unit 3910 may include synchronizationdata, such as temporal data, that may be used to maintainsynchronization, with respect to the collaboration context or session,among the low-latency database analysis system 3000 and one or moresystem access devices associated with, or otherwise accessing, thecollaboration context or session.

The third-party integration unit 3920 may include an electroniccommunication interface, such as an application programming interface(API), for interfacing or communicating between an external, such asthird-party, application or system, and the low-latency databaseanalysis system 3000. For example, the third-party integration unit 3920may include an electronic communication interface to transfer databetween the low-latency database analysis system 3000 and one or moreexternal applications or systems, such as by importing data into thelow-latency database analysis system 3000 from the external applicationsor systems or exporting data from the low-latency database analysissystem 3000 to the external applications or systems. For example, thethird-party integration unit 3920 may include an electroniccommunication interface for electronic communication with an externalexchange, transfer, load (ETL) system, which may import data into thelow-latency database analysis system 3000 from an external data sourceor may export data from the low-latency database analysis system 3000 toan external data repository. In another example, the third-partyintegration unit 3920 may include an electronic communication interfacefor electronic communication with external machine learning analysissoftware, which may export data from the low-latency database analysissystem 3000 to the external machine learning analysis software and mayimport data into the low-latency database analysis system 3000 from theexternal machine learning analysis software. The third-party integrationunit 3920 may transfer data independent of, or in conjunction with, thesystem access interface unit 3900, the enterprise data interface unit3400, or both.

FIG. 4 is a diagram of an example of a display region 4000 generated forpresenting a user interface to facilitate use of intelligent search andaction guidance. For example, display region 4000 may be a webpage thatincludes HTML and/or flash data. For example, a server may generate andpresent the display region 4000 by transmitting (e.g., via an electroniccommunications network) the display region 4000 to a user who receivesand views the display region 4000 using a client computing deviceconfigured to receive and display (e.g., using web browser application)the display region 4000. For example, the display region 4000 may bepart of a user interface that enables a user to access and viewvisualizations, charts, pinboards, and the like. The display region 4000includes a display pane 4100 that includes data for a currently viewedanalytical-object such as a visualization, and a modifications pane 4200that lists suggested modifications which may be selected to obtainsearch results that may be extensions of or related to the currentlyviewed visualization or search result. The modifications pane 4200 mayinclude, but is not limited to, a constrained search bar 4210, a list offilter modifications 4220, a list of comparative modifications 4230, andother suggested modifications. For example, the constrained search bar4210 may allow a user to enter input data that acts or operates on thecurrently viewed search result. Selection of a filter modification 4220or comparative modification 4230 may lead to actionable insights moreefficiently and effectively. Consequently, learning is provided in thecontext of a known visualization or search on how to use the filters,actions, or other modifications. For example, the display region 4000may be generated and presented by the internal database analysis portion2200 of FIG. 2, the computing device 1000 of FIG. 1, or the low-latencydatabase analysis system 3000 of FIG. 3. Although the description withrespect to the display region 4000 is with respect to a structuredsyntax grammar, it is applicable to a natural search grammar withoutdeparting from the scope of the specification and claims herein. Thedisplay region 4000 is illustrative and may include additional, fewer ordifferent presentations, icons, buttons, and the like which may besimilarly or differently architected without departing from the scope ofthe specification and claims herein.

The display region 4000 includes a display pane 4100 that includes datafor a currently viewed analytical-object such as a visualization. Forexample, the display pane 4100 may include processed data (e.g.,represented plots and/or summary text) based on data retrieved from adatabase using a resolved-request before and/or after modification ofthe resolved-request by the user.

The display region 4000 includes a modifications pane 4200 whichprovides suggested modifications that are selectable by the user toobtain additional search results which may be extensions of or relatedto the currently viewed visualization or search result. For example, thesuggested modifications may be a probabilistic determination based on avariety of factors including, but not limited to, search results, searchresult context, search result attributes, search result features,suggested modification usage, request for data logs, user profile,underlying data distribution, domain knowledge, context, device type,session, social signals, previous modifications, user feedback,diversity of suggested modifications, and the like. In animplementation, the modifications pane 4200 may include a constrainedsearch bar 4210 which may enable a user to enter a modificationoperating on the current visualization or search results. In animplementation, the modifications pane 4200 may include a list of filtermodifications 4220 which act upon the current visualization or searchresults. In an implementation, the modifications pane 4200 may include alist of comparative modifications 4230 which may present differentaspects of the current visualization or search results. In animplementation, one or more panes may be presented which may provide oneor more suggested modifications.

FIG. 5 is a block diagram of an example of a modification determinationunit 5000 in a low-latency database analysis system such as thelow-latency database analysis system 3000 of FIG. 3. In animplementation, the modification determination unit 5000 may beimplemented in or integrated with the relational search unit 3700. Themodification determination unit 5000 may receive multiple types of data5100, where each type of data may lead to one or more modifications5200.

In an implementation, the modification determination unit 5000 may makeone or more calls to request the data 5100 from the semantic interfaceunit 3600, data utility unit 3720, insight unit 3730, object utilityunit 3810, user customization unit 3830 and real-time collaboration unit3910, for example. The types of data 5100 may include, but is notlimited to, the current search results 5105, request for data historicaldata or logs 5110, modification usage 5115, underlying data distribution5120, domain knowledge 5125, context, device type, or sessioninformation 5130, social signals 5135, user curated modifications 5140,insights 5145, feature and token count data 5150, and the like.

In an implementation, the modification determination unit 5000 mayanalyze and process the multiple types of data 5100 to generate the oneor more modifications 5200. For example, the one or more modifications5200 may include, but is not limited to, additive modifications 5210,subtractive modifications 5220, filter modifications 5230, comparativemodifications 5240, historical modifications 5250, focus modifications5260, and the like.

In an implementation, the modification determination unit 5000 maygenerate modifications 5200 based on the data 5100, may use a variety ofscoring techniques to generate relevance rankings for the generatedmodifications, and may employ exploration-exploitation techniques toselect a diverse set of modifications for surfacing on the modificationspane 4200, for example. For example, the modification determination unit5000 may implement machine learning techniques which use decision trees,such as a Monte Carlo Tree Search (MCTS), in combination withprobabilistic decision making or reinforcement learning techniques.

For example, the relational search unit 3700 may request data from thedata utility unit 3720 for a visualization or search results currentlybeing outputted (e.g., currently being viewed by user). The receiveddata may include usage event counter data, feature-token pair countdata, utility data, and the like. The feature-token pair count data mayrepresent how many times a token appears in the context of a featuresuch as a phrase type, another token, and the like, for example. Themodification determination unit 5000 may generate, score, and rank themodifications based on the received data and other data obtained by themodification determination unit 5000 such as feedback data, modificationusage data, and the like.

For example, the modification determination unit 5000 may give firstorder relevance to tokens whose usage is counted in the context of thevisualization currently being viewed. In an implementation, themodification determination unit 5000 may give first order relevance totokens whose usage is counted in the context of the visualizationcurrently being viewed and the user, A threshold for the count may beused to account for errors such as mis-clicks and the like. In animplementation, the modification determination unit 5000 may give tokenswhose usage is counted in the context of the request for data that ispowering the visualization currently being viewed a second orderrelevance. In an implementation, the modification determination unit5000 may give tokens which are heavily used by the user a third orderrelevance. Although this type of token may be out of the context of thevisualization currently being viewed, if the token is in the same datascope and heavily used, it may still be relevant. For example, a userwho is in charge of a particular geographic region, may always want toapply the “geographic region” filter even though it may not be heavilyused in the context of the current visualization currently being viewed.In an implementation, the modification determination unit 5000 may givetokens which are heavily used globally but not as much in the context ofthe current user or visualization currently being viewed a fourth orderrelevance. The modification determination unit 5000 may then generatemodifications according to the computed scoring.

In an implementation, the modification determination unit 5000 may alsogenerate modifications based on usage patterns on the underlying data inthe visualization currently being viewed. In an implementation,templates may be used to determine modifications. For example, a datefilter modification may be generated if there is a date column in thedata. A date filter modification may be “last year”, “this year” and thelike. The relevant time periods for the date filter modification may bebased on time period(s) having best affinity to the visualizationcurrently being viewed, phrase index, a range of epochs, or the like.

In an implementation, the modification determination unit 5000 maygenerate modifications based on clustering. For example, the requests ofdata for visualizations, and the like may be grouped and saved asclusters. For a given visualization and the like, the modificationdetermination unit 5000 may pick a cluster which matches thevisualization currently being viewed and generates a modification whichguides the user towards the other visualizations in the cluster.

In an implementation, the modification determination unit 5000 maygenerate modifications based on user clustering, i.e. user collaborationfiltering. For example, users may be grouped into clusters based onusage journey and usage patterns. For example, for the visualizationcurrently being viewed, the modification determination unit 5000 mayidentify actions done by other users in the user cluster and generatemodifications accordingly. For example, modifications may be addition ordeletion of tokens, modifications used by other users for samevisualization, modifications used by other users for anothervisualization but are part of a request for data cluster as describedherein.

FIG. 6 is an example of modification generating 6000. The modificationgenerating 6000 includes requesting 6100 modification determination datafor a visualization currently being viewed, generating 6200modifications based on the modification determination data, scoring 6300the generated modifications to produce ranked modifications,diversifying 6400 the ranked modifications, and surfacing 6500 thediversified modifications. For example, the technique 6000 may beimplemented by the internal database analysis portion 2200 of FIG. 2,the computing device 1000 of FIG. 1, or the low-latency databaseanalysis system 3000 of FIG. 3.

The modification generating 6000 includes requesting 6100 modificationdetermination data for a visualization currently being viewed. Themodification determination data may include, but is not limited to,current search results, request for data historical data or logs,modification usage, underlying data distribution, domain knowledge,context, device type, or session information, social signals, usercurated modifications, insights, feature and token count data, utilitydata, usage data, feedback data, and the like. In an implementation, themodification determination data may be obtained for an analytical-objectcurrently being viewed.

The modification generating 6000 includes generating 6200 modificationsbased on the received modification determination data. In animplementation, different modifications may be generated based on themodification determination data. For example, the modifications mayinclude additive modifications, subtractive modifications, comparativemodifications, filter modifications, focus modifications, historicalmodifications, outlier modifications, and the like. The modificationsmay be search structures or search syntax which obtain search resultsthat may be extensions of or related to a visualization, search result,analytical-object or the like currently being viewed. In animplementation, pattern templates may be used to generate themodifications. For example, a time period may be present and a patterntemplate may suggest using the same time period from a previous year.

The modification generating 6000 includes scoring 6300 the generatedmodifications to produce ranked modifications. For example, modificationdetermination data such as, but not limited to, utility data forunderlying data, usage data for modifications, outlier information,historical information, user selected modifications, cluster data, andthe like may be used to generate a relevance score for each generatedmodification. In an implementation, weighting may be used to score thegenerated modifications. For example, the usage data for modificationsmay be weighted higher than cluster data. In an implementation, adefined number of highest ranked modifications are selected.

The modification generating 6000 includes diversifying 6400 the rankedmodifications. A diverse set of modifications may be generated by usingmodification determination data such as, but not limited to, userselected modifications, insights, outlier information, feedback data,and the like to generate a set of modifications having width and breadthin modification types. For example, the diversification may include atleast one of additive modifications, subtractive modifications,comparative modifications, filter modifications, focus modifications,historical modifications, outlier modifications, and the like. In animplementation, a defined number of diverse modifications are selected.In an implementation, certain modification types may have multiplemodifications in the selected set of modifications. In animplementation, weighting may be used to diversify the scoredmodifications. For example, positive feedback data or modificationsusage data for a modification may increase a weighting factor for thatmodification. In an implementation, a defined number of rankedmodifications are selected.

The modification generating 6000 includes outputting 6500 a presentationof the diversified modifications. The selected modifications may berendered or presented on a system access unit.

As used herein, the terminology “computer” or “computing device”includes any unit, or combination of units, capable of performing anymethod, or any portion or portions thereof, disclosed herein.

As used herein, the terminology “processor” indicates one or moreprocessors, such as one or more special purpose processors, one or moredigital signal processors, one or more microprocessors, one or morecontrollers, one or more microcontrollers, one or more applicationprocessors, one or more central processing units (CPU)s, one or moregraphics processing units (GPU)s, one or more digital signal processors(DSP)s, one or more application specific integrated circuits (ASIC)s,one or more application specific standard products, one or more fieldprogrammable gate arrays, any other type or combination of integratedcircuits, one or more state machines, or any combination thereof.

As used herein, the terminology “memory” indicates any computer-usableor computer-readable medium or device that can tangibly contain, store,communicate, or transport any signal or information that may be used byor in connection with any processor. For example, a memory may be one ormore read only memories (ROM), one or more random access memories (RAM),one or more registers, low power double data rate (LPDDR) memories, oneor more cache memories, one or more semiconductor memory devices, one ormore magnetic media, one or more optical media, one or moremagneto-optical media, or any combination thereof.

As used herein, the terminology “instructions” may include directions orexpressions for performing any method, or any portion or portionsthereof, disclosed herein, and may be realized in hardware, software, orany combination thereof. For example, instructions may be implemented asinformation, such as a computer program, stored in memory that may beexecuted by a processor to perform any of the respective methods,algorithms, aspects, or combinations thereof, as described herein.Instructions, or a portion thereof, may be implemented as a specialpurpose processor, or circuitry, that may include specialized hardwarefor carrying out any of the methods, algorithms, aspects, orcombinations thereof, as described herein. In some implementations,portions of the instructions may be distributed across multipleprocessors on a single device, on multiple devices, which maycommunicate directly or across a network such as a local area network, awide area network, the Internet, or a combination thereof.

As used herein, the terminology “determine,” “identify,” “obtain,” and“form” or any variations thereof, includes selecting, ascertaining,computing, looking up, receiving, determining, establishing, obtaining,or otherwise identifying or determining in any manner whatsoever usingone or more of the devices and methods shown and described herein.

As used herein, the term “computing device” includes any unit, orcombination of units, capable of performing any method, or any portionor portions thereof, disclosed herein.

As used herein, the terminology “example,” “embodiment,”“implementation,” “aspect,” “feature,” or “element” indicates serving asan example, instance, or illustration. Unless expressly indicated, anyexample, embodiment, implementation, aspect, feature, or element isindependent of each other example, embodiment, implementation, aspect,feature, or element and may be used in combination with any otherexample, embodiment, implementation, aspect, feature, or element.

As used herein, the terminology “or” is intended to mean an inclusive“or” rather than an exclusive “or” That is, unless specified otherwise,or clear from context, “X includes A or B” is intended to indicate anyof the natural inclusive permutations. That is, if X includes A; Xincludes B; or X includes both A and B, then “X includes A or B” issatisfied under any of the foregoing instances. In addition, thearticles “a” and “an” as used in this application and the appendedclaims should generally be construed to mean “one or more” unlessspecified otherwise or clear from the context to be directed to asingular form.

Further, for simplicity of explanation, although the figures anddescriptions herein may include sequences or series of steps or stages,elements of the methods disclosed herein may occur in various orders orconcurrently. Additionally, elements of the methods disclosed herein mayoccur with other elements not explicitly presented and described herein.Furthermore, not all elements of the methods described herein may berequired to implement a method in accordance with this disclosure.Although aspects, features, and elements are described herein inparticular combinations, each aspect, feature, or element may be usedindependently or in various combinations with or without other aspects,features, and elements.

Although some embodiments herein refer to methods, it will beappreciated by one skilled in the art that they may also be embodied asa system or computer program product. Accordingly, aspects of thepresent invention may take the form of an entirely hardware embodiment,an entirely software embodiment (including firmware, resident software,micro-code, etc.) or an embodiment combining software and hardwareaspects that may all generally be referred to herein as a “processor,”“device,” or “system.” Furthermore, aspects of the present invention maytake the form of a computer program product embodied in one or morecomputer readable mediums having computer readable program code embodiedthereon. Any combination of one or more computer readable mediums may beutilized. The computer readable medium may be a computer readable signalmedium or a computer readable storage medium. A computer readablestorage medium may be, for example, but not limited to, an electronic,magnetic, optical, electromagnetic, infrared, or semiconductor system,apparatus, or device, or any suitable combination of the foregoing. Morespecific examples (a non-exhaustive list) of the computer readablestorage medium include the following: an electrical connection havingone or more wires, a portable computer diskette, a hard disk, a randomaccess memory (RAM), a read-only, memory (ROM), an erasable programmableread-only memory (EPROM or Flash memory), an optical fiber, a portablecompact disc read-only memory (CD-ROM), an optical storage device, amagnetic storage device, or any suitable combination of the foregoing.In the context of this document, a computer readable storage medium maybe any tangible medium that can contain or store a program for use by orin connection with an instruction execution system, apparatus, ordevice.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave, Such a propagated signal may takeany of a variety of forms, including, but not limited to,electro-magnetic, optical, or any suitable combination thereof. Acomputer readable signal medium may be any computer readable medium thatis not a computer readable storage medium and that can communicate,propagate, or transport a program for use by or in connection with aninstruction execution system, apparatus, or device.

Program code embodied on a computer readable medium may be transmittedusing any appropriate medium, including but not limited to CDs, DVDs,wireless, wireline, optical fiber cable, RE, etc., or any suitablecombination of the foregoing.

Computer program code for carrying out operations for aspects of thepresent invention may be written in any combination of one or moreprogramming languages, including an object-oriented programming languagesuch as Java, Smalltalk, C++ or the like and conventional proceduralprogramming languages, such as the “C” programming language or similarprogramming languages. The program code may execute entirely on theuser's computer, partly on the user's computer, as a stand-alonesoftware package, partly on the user's computer and partly on a remotecomputer or entirely on the remote computer or server. In the latterscenario, the remote computer may be connected to the user's computerthrough any type of network, including a local area network (LAN) or awide area network (WAN), or the connection may be made to an externalcomputer (for example, through the Internet using an Internet ServiceProvider).

Attributes may comprise any data characteristic, category, content, etc.that in one example may be non-quantifiable or non-numeric. Measures maycomprise quantifiable numeric values such as sizes, amounts, degrees,etc. For example, a first column containing the names of states may beconsidered an attribute column and a second column containing thenumbers of orders received for the different states may be considered ameasure column.

Aspects of the present embodiments are described above with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems) and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer program instructions. These computer program instructions maybe provided to a processor of a computer, such as a special purposecomputer, or other programmable data processing apparatus to produce amachine, such that the instructions, which execute via the processor ofthe computer or other programmable data processing apparatus, createmeans for implementing the functions/acts specified in the flowchartand/or block diagram block or blocks. These computer programinstructions may also be stored in a computer readable medium that candirect a computer, other programmable data processing apparatus, orother devices to function in a particular manner, such that theinstructions stored in the computer readable medium produce an articleof manufacture including instructions which implement the function/actspecified in the flowchart and/or block diagram block or blocks. Thecomputer program instructions may also be loaded onto a computer, otherprogrammable data processing apparatus, or other devices to cause aseries of operational steps to be performed on the computer, otherprogrammable apparatus or other devices to produce a computerimplemented process such that the instructions which execute on thecomputer or other programmable apparatus provide processes forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. The flowcharts and block diagrams in thefigures illustrate the architecture, functionality, and operation ofpossible implementations of systems, methods and computer programproducts according to various embodiments of the present invention. Inthis regard, each block in the flowchart or block diagrams may representa module, segment, or portion of code, which comprises one or moreexecutable instructions for implementing the specified logicalfunction(s). It should also be noted that, in some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts, or combinations of special purpose hardware andcomputer instructions.

While the disclosure has been described in connection with certainembodiments, it is to be understood that the disclosure is not to belimited to the disclosed embodiments but, on the contrary, is intendedto cover various modifications and equivalent arrangements includedwithin the scope of the appended claims, which scope is to be accordedthe broadest interpretation so as to encompass all such modificationsand equivalent structures as is permitted under the law.

What is claimed is:
 1. A method for use in a low-latency databaseanalysis system, the method comprising: obtaining, by a low-latencydatabase analysis system, data expressing a usage intent with respect tothe low-latency database analysis system; in response to obtaining thedata expressing the usage intent, obtaining an analytical-objectrepresenting a request for data indicated in the data expressing theusage intent; in response to obtaining the analytical-object, obtainingfirst results data responsive to the request for data; in response toobtaining the first results data, outputting visualization data forpresenting a visualization representing the first results data; inresponse to outputting the visualization data: generating a diverseplurality of candidate modifications for the request for data including:a first candidate modification for modifying the request for data inaccordance with a first modification type; and a second candidatemodification for modifying the request for data in accordance with asecond modification type that differs from the first modification type;and outputting candidate modification data for presenting the diverseplurality of candidate modifications in association with presenting thevisualization representing the results data; in response to outputtingthe candidate modification data, obtaining user input data identifying acandidate modification from the diverse plurality of candidatemodifications as a selected modification; in response to obtaining theuser input data, executing a data-query expressing the request for datamodified by the selected modification to obtain second results dataresponsive to the request for data modified by the selectedmodification; and in response to obtaining the second results data,outputting visualization data for presenting a visualizationrepresenting the second results data.
 2. The method of claim 1, whereingenerating the diverse plurality of candidate modifications includesgenerating the diverse plurality of candidate modifications inaccordance with a defined plurality of modification types.
 3. The methodof claim 2, wherein the defined plurality of modification types includesan additive modification type, a subtractive modification type, acomparative modification type, a filter modification type, a focusmodification type, a historical modification type, and an outliermodification type.
 4. The method of claim 3, wherein generating thediverse plurality of candidate modifications includes generating thefirst candidate modification such that the first candidate modificationis a comparative modification indicating a comparison with respect to afirst value and a second value indicated in the results data.
 5. Themethod of claim 1, wherein generating the diverse plurality of candidatemodifications includes generating the first candidate modification suchthat the first candidate modification includes an input element formodifying the request for data by adding a filter or a column.
 6. Themethod of claim 1, wherein generating the diverse plurality of candidatemodifications includes generating the first candidate modification suchthat, in response to determining that the results data includes a datecolumn, the first candidate modification includes a date filtermodification.
 7. The method of claim 1, wherein generating the diverseplurality of candidate modifications includes: identifying a geographicregion associated with a user associated with the request for data; andgenerating the first candidate modification such that the firstcandidate modification includes a geographic region modification inaccordance with the geographic region.
 8. A low-latency databaseanalysis system, comprising: a memory that stores executableinstructions; and a processor that executes the instructions to: obtaindata expressing a usage intent with respect to the low-latency databaseanalysis system; in response to the data expressing the usage intent,obtain an analytical-object representing a request for data indicated inthe data expressing the usage intent; in response to theanalytical-object, obtain first results data responsive to the requestfor data; in response to the first results data, output visualizationdata for presenting a visualization representing the first results data;in response to outputting the visualization data: generate a diverseplurality of candidate modifications for the request for data including:a first candidate modification for modifying the request for data inaccordance with a first modification type; and a second candidatemodification for modifying the request for data in accordance with asecond modification type that differs from the first modification type;and output candidate modification data for presenting the diverseplurality of candidate modifications in association with thepresentation of the visualization representing the results data; inresponse to outputting the candidate modification data, obtain userinput data identifying a candidate modification from the diverseplurality of candidate modifications as a selected modification; inresponse to the user input data, execute a data-query expressing therequest for data modified by the selected modification to obtain secondresults data responsive to the request for data modified by the selectedmodification; and in response to the second results data, outputvisualization data for presenting a visualization representing thesecond results data.
 9. The low-latency database analysis system ofclaim 8, wherein generating the diverse plurality of candidatemodifications includes generating the diverse plurality of candidatemodifications in accordance with a defined plurality of modificationtypes.
 10. The low-latency database analysis system of claim 9, whereinthe defined plurality of modification types includes an additivemodification type, a subtractive modification type, a comparativemodification type, a filter modification type, a focus modificationtype, a historical modification type, and an outlier modification type.11. The low-latency database analysis system of claim 10, whereingenerating the diverse plurality of candidate modifications includesgenerating the first candidate modification such that the firstcandidate modification is a comparative modification indicating acomparison with respect to a first value and a second value indicated inthe results data.
 12. The low-latency database analysis system of claim8, wherein generating the diverse plurality of candidate modificationsincludes generating the first candidate modification such that the firstcandidate modification includes an input element for modifying therequest for data by adding a filter or a column.
 13. The low-latencydatabase analysis system of claim 8, wherein generating the diverseplurality of candidate modifications includes generating the firstcandidate modification such that, in response to determining that theresults data includes a date column, the first candidate modificationincludes a date filter modification.
 14. The low-latency databaseanalysis system of claim 8, wherein generating the diverse plurality ofcandidate modifications includes: identifying a geographic regionassociated with a user associated with the request for data; andgenerating the first candidate modification such that the firstcandidate modification includes a geographic region modification inaccordance with the geographic region.
 15. A non-transitorycomputer-readable storage medium, comprising executable instructionssuch that execution of the instructions by a processor includes:obtaining, by a low-latency database analysis system, data expressing ausage intent with respect to the low-latency database analysis system;in response to obtaining the data expressing the usage intent, obtainingan analytical-object representing a request for data indicated in thedata expressing the usage intent; in response to obtaining theanalytical-object; obtaining first results data responsive to therequest for data; in response to obtaining the first results data,outputting visualization data for presenting a visualizationrepresenting the first results data; in response to outputting thevisualization data: generating a diverse plurality of candidatemodifications for the request for data including: a first candidatemodification for modifying the request for data in accordance with afirst modification type; and a second candidate modification formodifying the request for data in accordance with a second modificationtype that differs from the first modification type; and outputtingcandidate modification data for presenting the diverse plurality ofcandidate modifications in association with presenting the visualizationrepresenting the results data; in response to outputting the candidatemodification data, obtaining user input data identifying a candidatemodification from the diverse plurality of candidate modifications as aselected modification; in response to obtaining the user input data,executing a data-query expressing the request for data modified by theselected modification to obtain second results data responsive to therequest for data modified by the selected modification; and in responseto obtaining the second results data, outputting visualization data forpresenting a visualization representing the second results data.
 16. Thenon-transitory computer-readable storage medium of claim 15, whereingenerating the diverse plurality of candidate modifications includesgenerating the diverse plurality of candidate modifications inaccordance with a defined plurality of modification types.
 17. Thenon-transitory computer-readable storage medium of claim 16, wherein thedefined plurality of modification types includes an additivemodification type, a subtractive modification type, a comparativemodification type, a filter modification type, a focus modificationtype, a historical modification type, and an outlier modification type.18. The non-transitory computer-readable storage medium of claim 17,wherein generating the diverse plurality of candidate modificationsincludes generating the first candidate modification such that the firstcandidate modification is a comparative modification indicating acomparison with respect to a first value and a second value indicated inthe results data.
 19. The non-transitory computer-readable storagemedium of claim 15, wherein generating the diverse plurality ofcandidate modifications includes generating the first candidatemodification such that the first candidate modification includes aninput element for modifying the request for data by adding a filter or acolumn.
 20. The non-transitory computer-readable storage medium of claim15, wherein generating the diverse plurality of candidate modificationsincludes generating the first candidate modification such that, inresponse to determining that the results data includes a date column,the first candidate modification includes a date filter modification.